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1
Guney, C. "RETHINKING INDOOR LOCALIZATION SOLUTIONS TOWARDS THE FUTURE OF MOBILE LOCATION-BASED SERVICES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 235–47. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-235-2017.
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Satellite navigation systems with GNSS-enabled devices, such as smartphones, car navigation systems, have changed the way users travel in outdoor environment. GNSS is generally not well suited for indoor location and navigation because of two reasons: First, GNSS does not provide a high level of accuracy although indoor applications need higher accuracies. Secondly, poor coverage of satellite signals for indoor environments decreases its accuracy. So rather than using GNSS satellites within closed environments, existing indoor navigation solutions rely heavily on installed sensor networks. There is a high demand for accurate positioning in wireless networks in GNSS-denied environments. However, current wireless indoor positioning systems cannot satisfy the challenging needs of indoor location-aware applications. Nevertheless, access to a user’s location indoors is increasingly important in the development of context-aware applications that increases business efficiency. In this study, how can the current wireless location sensing systems be tailored and integrated for specific applications, like smart cities/grids/buildings/cars and IoT applications, in GNSS-deprived areas.
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Kim Geok, Tan, Khaing Zar Aung, Moe Sandar Aung, Min Thu Soe, Azlan Abdaziz, Chia Pao Liew, Ferdous Hossain, Chih P. Tso, and Wong Hin Yong. "Review of Indoor Positioning: Radio Wave Technology." Applied Sciences 11, no. 1 (December 30, 2020): 279. http://dx.doi.org/10.3390/app11010279.
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The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.
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Karakusak, Muhammed Zahid, Hasan Kivrak, Hasan Fehmi Ates, and Mehmet Kemal Ozdemir. "RSS-Based Wireless LAN Indoor Localization and Tracking Using Deep Architectures." Big Data and Cognitive Computing 6, no. 3 (August 8, 2022): 84. http://dx.doi.org/10.3390/bdcc6030084.
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Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2m) under the same experiment environment.
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Chirantan Ganguly, Sagnik Nayak, S. Irene, Anil Kumar Gupta, Suresh V., and Pradeep Kumar CH. "Utilizing machine learning algorithms for localization using RSSI values of wireless LAN." ITU Journal on Future and Evolving Technologies 3, no. 2 (June 17, 2022): 98–107. http://dx.doi.org/10.52953/mvre7314.
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With the development of new technologies, there has been an upsurge in the demand for precise localization in both outdoor and indoor environments. While a Global Positioning System (GPS) provides sufficient positioning precision in outdoor settings, its accuracy declines in indoor scenarios, necessitating the development of novel positioning approaches that function accurately both indoors and outdoors. The use of various Wireless Local Area Network (WLAN) parameters for localization has been conceptualized. In this study, we attempt to do localization using machine learning methods on WLAN Received Signal Strength Indicator (WLAN RSSI) measurements. We compare the performance of multiple machine learning algorithms on the data set to see which can be used to design efficient future localization systems. The proposed study has achieved second place for the problem statement "ITU-ML5G-PS-016: Location estimation using RSSI of wireless LAN" in AI/ML in 5G Challenge 2021 organized by the International Telecommunication Union.
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Khudhair, Ahmed Azeez, Saba Qasim Jabbar, Mohammed Qasim Sulttan, and Desheng Wang. "Wireless Indoor Localization Systems and Techniques: Survey and Comparative Study." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 2 (August 1, 2016): 392. http://dx.doi.org/10.11591/ijeecs.v3.i2.pp392-409.
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<p>The popularity, great influence and huge importance made wireless indoor localization has a unique touch, as well its wide successful on positioning and tracking systems for both human and assists also contributing to take the lead from outdoor systems in the scope of the recent research works. In this work, we will attempt to provide a survey of the existing indoor positioning solutions and attempt to classify different its techniques and systems. Five typical location predication approaches (triangulation, fingerprinting, proximity, vision analysis and trilateration) are considered here in order to analysis and provide the reader a review of the recent advances in wireless indoor localization techniques and systems to have a good understanding of state of the art technologies and motivate new research efforts in this promising direction. For these reasons, existing wireless localization position systems and location estimation schemes are reviewed. We also made a comparison among the related techniques and systems along with conclusions and future trends to identify some possible areas of enhancements. </p>
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Sung, Kwangjae, Hyung Kyu Lee, and Hwangnam Kim. "Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks." Sensors 19, no. 18 (September 10, 2019): 3907. http://dx.doi.org/10.3390/s19183907.
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The indoor pedestrian positioning methods are affected by substantial bias and errors because of the use of cheap microelectromechanical systems (MEMS) devices (e.g., gyroscope and accelerometer) and the users’ movements. Moreover, because radio-frequency (RF) signal values are changed drastically due to multipath fading and obstruction, the performance of RF-based localization systems may deteriorate in practice. To deal with this problem, various indoor localization methods that integrate the positional information gained from received signal strength (RSS) fingerprinting scheme and the motion of the user inferred by dead reckoning (DR) approach via Bayes filters have been suggested to accomplish more accurate localization results indoors. Among the Bayes filters, while the particle filter (PF) can offer the most accurate positioning performance, it may require substantial computation time due to use of many samples (particles) for high positioning accuracy. This paper introduces a pedestrian localization scheme performed on a mobile phone that leverages the RSS fingerprint-based method, dead reckoning (DR), and improved PF called a double-stacked particle filter (DSPF) in indoor environments. As a key element of our system, the DSPF algorithm is employed to correct the position of the user by fusing noisy location data gained by the RSS fingerprinting and DR schemes. By estimating the position of the user through the proposal distribution and target distribution obtained from multiple measurements, the DSPF method can offer better localization results compared to the Kalman filtering-based methods, and it can achieve competitive localization accuracy compared with PF while offering higher computational efficiency than PF. Experimental results demonstrate that the DSPF algorithm can achieve accurate and reliable localization with higher efficiency in computational cost compared with PF in indoor environments.
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Hall, Donald L., Ram M. Narayanan, and David M. Jenkins. "SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning." Electronics 8, no. 11 (November 10, 2019): 1323. http://dx.doi.org/10.3390/electronics8111323.
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Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.
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Shubham Khunteta, Ashok Kumar Reddy Chavva, and Avani Agrawal. "AI-based indoor localization using mmWave MIMO channel at 60 GHz." ITU Journal on Future and Evolving Technologies 3, no. 2 (September 22, 2022): 243–51. http://dx.doi.org/10.52953/aorf8087.
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In recent years, indoor localization using wireless systems has been an important area of research for its applications towards health, security and the tracking of users. A Global Positioning System (GPS) is considered as the best solution for localization for outdoor scenarios but it fails to provide accurate positioning for indoor scenarios. Wi-Fi fingerprinting methods using received signal strength from multiple access points are popular for solving indoor localization problem. As the wireless systems move towards higher frequencies, higher bandwidth and a large antenna array, sensing has also become feasible along with communication, which is an important research area towards 6G named as Integrated Communication And Sensing (ISAC). ISAC relies on sensing parameter estimations, such as estimation of fine range, Doppler and angular information which contains the signature of the surrounding objects. A localization problem can be solved by analysing the sensing parameters. In this paper, we propose a solution for the localization problem for IEEE 802.11ay WLAN systems based on signal processing and Machine Learning (ML) in indoor scenarios. (...)
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Nor Hisham, Aina Nadhirah, Yin Hoe Ng, Chee Keong Tan, and David Chieng. "Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts." Data 7, no. 11 (November 9, 2022): 156. http://dx.doi.org/10.3390/data7110156.
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Indoor positioning has garnered significant interest over the last decade due to the rapidly growing demand for location-based services. As a result, a multitude of techniques has been proposed to localize objects and devices in indoor environments. Wireless fingerprinting, which leverages machine learning, has emerged as one of the most popular positioning approaches due to its low implementation cost. The prevailing fingerprinting-based positioning mainly utilizes wireless fidelity (Wi-Fi) and Bluetooth low energy (BLE) signals. However, the RSS of Wi-Fi and BLE signals are very sensitive to the layout of the indoor environment. Thus, any change in the indoor layout could potentially lead to severe degradation in terms of localization performance. To foster the development of new positioning methods, several open-source location fingerprinting datasets have been made available to the research community. Unfortunately, none of these public datasets provides the received signal strength (RSS) measurements for indoor environments with different layouts. To fill this gap, this paper presents a new hybrid Wi-Fi and BLE fingerprinting dataset for multi-floor indoor environments with different layouts to facilitate the future development of new fingerprinting-based positioning systems that can provide adaptive positioning performance in dynamic indoor environments. Additionally, the effects of indoor layout change on the location fingerprint and localization performance are also investigated.
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Farid, Zahid, Rosdiadee Nordin, Mahamod Ismail, and Nor Fadzilah Abdullah. "Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6923931.
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In indoor environments, WiFi (RSS) based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies) in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs). This model exploits machine learning, in particular Artificial Natural Network (ANN) techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA) based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.
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Rahman, A. B. M. Mohaimenur, Ting Li, and Yu Wang. "Recent Advances in Indoor Localization via Visible Lights: A Survey." Sensors 20, no. 5 (March 3, 2020): 1382. http://dx.doi.org/10.3390/s20051382.
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Because of the limitations of the Global Positioning System (GPS) in indoor scenarios, various types of indoor positioning or localization technologies have been proposed and deployed. Wireless radio signals have been widely used for both communication and localization purposes due to their popular availability in indoor spaces. However, the accuracy of indoor localization based purely on radio signals is still not perfect. Recently, visible light communication (VLC) has made use of electromagnetic radiation from light sources for transmitting data. The potential for deploying visible light communication for indoor localization has been investigated in recent years. Visible-light-based localization enjoys low deployment cost, high throughput, and high security. In this article, the most recent advances in visible-light-based indoor localization systems have been reviewed. We strongly believe that visible-light-based localization will become a low-cost and feasible complementary solution for indoor localization and other smart building applications.
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Bai, Lu, Chenglie Du, and Jinchao Chen. "Weighted K-nearest Neighbor Fast Localization Algorithm Based on RSSI for Wireless Sensor Systems." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 2 (April 27, 2020): 295–301. http://dx.doi.org/10.2174/2352096512666191024170807.
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Background: Wireless positioning is one of the most important technologies for realtime applications in wireless sensor systems. This paper mainly studies the indoor wireless positioning algorithm of robots. Methods: The application of the K-nearest neighbor algorithm in Wi-Fi positioning is studied by analyzing the Wi-Fi fingerprint location algorithm based on Received Signal Strength Indication (RSSI) and K-Nearest Neighbor (KNN) algorithm in Wi-Fi positioning. The KNN algorithm is computationally intensive and time-consuming. Results: In order to improve the positioning efficiency, improve the positioning accuracy and reduce the computation time, a fast weighted K-neighbor correlation algorithm based on RSSI is proposed based on the K-Means algorithm. Thereby achieving the purpose of reducing the calculation time, quickly estimating the position distance, and improving the positioning accuracy. Conclusion: Simulation analysis shows that the algorithm can effectively shorten the positioning time and improve the positioning efficiency in robot Wi-Fi positioning.
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Wang, Youqing, Kun Zhao, and Zhengqi Zheng. "A 3D Indoor Positioning Method of Wireless Network with Single Base Station in Multipath Environment." Wireless Communications and Mobile Computing 2022 (April 14, 2022): 1–13. http://dx.doi.org/10.1155/2022/3144509.
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The proliferation of indoor location-based services has increased the demand of indoor positioning technology. Severe multipath and coherence effects are the difference between signal propagation indoors and outdoors. Most existing indoor localization methods build their models in 2-dimensional space and try to avoid the influence of multipath. We propose a method to realize 3-dimensional indoor positioning with single base station by using multipath channel. The angles of multipath coherent signals are estimated by MIMO antenna and the delays are estimated by OFDM signal. To avoid the complicated calculation in joint estimation of angles and delays in 3-dimensional space, the angles and delays are estimated separately and matched by the proposed algorithm. The line-of-sight channel is differentiated by time delay, and the reflection paths for non-line-of-sight channels are established with angle information and indoor maps. Finally, combine the angle information of the reflection paths and the line-of-sight path to obtain the target position in 3-dimensional indoor space. We verified the method through simulation in an indoor space of 6 m × 8 m × 4.5 m . The positioning errors are submeter level in 95 % cases and less than 0.4m in 60 % cases. The proposed method requires only one base station and can be applied in most wireless networks. Compared with existing indoor localization methods, it has lower computational complexity and higher application potential.
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Isaia, Constantina, and Michalis P. Michaelides. "A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G." Signals 4, no. 1 (January 28, 2023): 90–136. http://dx.doi.org/10.3390/signals4010006.
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In recent years, tremendous advances have been made in the design and applications of wireless networks and embedded sensors. The combination of sophisticated sensors with wireless communication has introduced new applications, which can simplify humans’ daily activities, increase independence, and improve quality of life. Although numerous positioning techniques and wireless technologies have been introduced over the last few decades, there is still a need for improvements, in terms of efficiency, accuracy, and performance for the various applications. Localization importance increased even more recently, due to the coronavirus pandemic, which made people spend more time indoors. Improvements can be achieved by integrating sensor fusion and combining various wireless technologies for taking advantage of their individual strengths. Integrated sensing is also envisaged in the coming technologies, such as 6G. The primary aim of this review article is to discuss and evaluate the different wireless positioning techniques and technologies available for both indoor and outdoor localization. This, in combination with the analysis of the various discussed methods, including active and passive positioning, SLAM, PDR, integrated sensing, and sensor fusion, will pave the way for designing the future wireless positioning systems.
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Cheng, Chia-Hsin, and Yi Yan. "Indoor positioning system for wireless sensor networks based on two-stage fuzzy inference." International Journal of Distributed Sensor Networks 14, no. 5 (May 2018): 155014771878064. http://dx.doi.org/10.1177/1550147718780649.
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Wireless indoor positioning systems are susceptible to environmental distortion and attenuation of the signal, which can affect positioning accuracy. In this article, we present a two-stage indoor positioning scheme using a fuzzy-based algorithm aimed at minimizing uncertainty in received signal strength indicator measures from reference nodes in wireless sensor networks. In the first stage, the indoor space is divided into several zones and a fuzzy-based indoor zone-positioning scheme is used to identify the zone in which the target node is located via zone splitting. In the second stage, adaptive trilateration is used to position the target node within the zone identified in the first stage. Simulation results demonstrate that the proposed two-stage fuzzy rectangular splitting outperforms non-fuzzy-based algorithms, including K-Nearest Neighbors–based localization, and traditional triangular splitting schemes. We also developed an expanded positioning scheme to facilitate the selection of a positioning map for large indoor spaces, thereby overcoming the limitations imposed by the size of the positioning area while maintaining high positioning resolution.
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Pan, Su, Sheng Hua, Duowei Pan, and Xixia Sun. "Wireless Localization Method Based on AHP-WKNN and Amendatory AKF." Wireless Communications and Mobile Computing 2021 (May 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/8859731.
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In this paper, we propose an AHP-WKNN method for indoor localization which combines the Analytic Hierarchy Process (AHP) technique and the Weighted K -nearest Neighbor (WKNN) algorithm. AHP serves to assign weights when WKNN is employed to select fingerprints for indoor positioning. The AHP technique can reasonably enlarge the influence that the received signal strength (RSS) gap between reference points has on the weights, achieving better performance in positioning. This paper also modifies the adaptive Kalman filter (AKF) noise reduction method by correcting the output based on the error between the RSS measurement and the expected output. The modified AKF can track the changes of RSS more effectively and achieve better performance of noise reduction. The simulation result shows that the proposed AHP-WKNN method and the modified AKF can improve positioning accuracy effectively.
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Deng, Zhongliang, Xiao Fu, Qianqian Cheng, Lingjie Shi, and Wen Liu. "CC-DTW: An Accurate Indoor Fingerprinting Localization Using Calibrated Channel State Information and Modified Dynamic Time Warping." Sensors 19, no. 9 (April 28, 2019): 1984. http://dx.doi.org/10.3390/s19091984.
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Indoor wireless local area network (WLAN) based positioning technologies have boomed recently because of the huge demands of indoor location-based services (ILBS) and the wide deployment of commercial Wi-Fi devices. Channel state information (CSI) extracted from Wi-Fi signals could be calibrated and utilized as a fine-grained positioning feature for indoor fingerprinting localization. One of the main factors that would restrict the positioning accuracy of fingerprinting systems is the spatial resolution of fingerprints (SRF). This paper mainly focuses on the improvement of SRF for indoor CSI-based positioning and a calibrated CSI feature (CCF) with high SRF is established based on the preprocess of both measured amplitude and phase. In addition, a similarity calculation metric for the proposed CCF is designed based on modified dynamic time warping (MDTW). An indoor fingerprinting method based on CCF and MDTW, named CC-DTW, is then proposed to improve the positioning accuracy in indoors. Experiments are conducted in two indoor office testbeds, and the performances of the proposed CC-DTW, one time-reversal (TR) based approach and one Euclidean distance (ED) based approach are evaluated and discussed. The results show that the SRF of CC-DTW outperforms the TR-based one and the ED-based one in both two testbeds in terms of the receiver operating characteristic (ROC) curve metric, and the area under curve (AUC) metric.
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Zhang, Xing, Lin Ma, Xue Zhi Tan, and Shi Zeng Guo. "A Novel Algorithm Based on Clustering and Access Points Selection for Indoor Fingerprint Localization." Advanced Materials Research 756-759 (September 2013): 3527–31. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3527.
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With the growing popularity of location-based service (LBS), wireless local area networks (WLAN) indoor positioning has gained widespread attention. Unlike the traditional algorithm concentrating on positioning accuracy, we discuss how to improve the real-time property in WLAN indoor fingerprinting localization systems. In this paper, we present a novel algorithm which first divides the positioning area into sub-areas utilizing k-means clustering, and then selects appropriate access points (APs) for positioning to make the calculated amount as less as possible. By collecting data and performing in the real WLAN environment, our proposed algorithm shows high positioning accuracy while the computational burden has been decreased almost 93.7%.
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Farid, Zahid, Rosdiadee Nordin, and Mahamod Ismail. "Recent Advances in Wireless Indoor Localization Techniques and System." Journal of Computer Networks and Communications 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/185138.
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The advances in localization based technologies and the increasing importance of ubiquitous computing and context-dependent information have led to a growing business interest in location-based applications and services. Today, most application requirements are locating or real-time tracking of physical belongings inside buildings accurately; thus, the demand for indoor localization services has become a key prerequisite in some markets. Moreover, indoor localization technologies address the inadequacy of global positioning system inside a closed environment, like buildings. Based on this, though, this paper aims to provide the reader with a review of the recent advances in wireless indoor localization techniques and system to deliver a better understanding of state-of-the-art technologies and motivate new research efforts in this promising field. For this purpose, existing wireless localization position system and location estimation schemes are reviewed, as we also compare the related techniques and systems along with a conclusion and future trends.
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Subedi, Santosh, and Jae-Young Pyun. "A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies." Sensors 20, no. 24 (December 17, 2020): 7230. http://dx.doi.org/10.3390/s20247230.
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In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.
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Jumaah, Al-Nussairi Ahmed Kateb. "Enhanced Least Square Method for Indoor Positioning System Using UWB Technology." Webology 19, no. 1 (January 20, 2022): 3815–34. http://dx.doi.org/10.14704/web/v19i1/web19251.
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One of the main radio technologies that could be used for indoor localization is Ultra-wideband, (UWB). It is a short-range RF technology for wireless communication that can be leveraged to detect the location of people, devices, and assets with significant precision. But, it has a major limitation which is the need for a non-line-of-sight (NLOS) identification and mitigation approach to precise location a target in a hard indoor environment. The NLOS approach will complicate the positioning approach. The goals of this work are; i- for saving cost and time of installation of anchor nodes, the minimum required number of anchor nodes have been installed, ii- the accuracy of the created system should be compatible with most various indoor environments. In this work, we create a novel algorithm of Indoor positioning system named Enhanced Linearized Least Square (ELLS) using UWB technology without using an NLOS identification approach. We evaluate and validate the created system by implementing real experiments. The created system has an average positioning accuracy reaching about 0.45 𝑚2of mean square error in a hard environment. It outperforms most indoor positioning systems in the market with less complexity, cost, and more accuracy.
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Sesyuk, Andrey, Stelios Ioannou, and Marios Raspopoulos. "A Survey of 3D Indoor Localization Systems and Technologies." Sensors 22, no. 23 (December 1, 2022): 9380. http://dx.doi.org/10.3390/s22239380.
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Indoor localization has recently and significantly attracted the interest of the research community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically fail in indoor environments. In the last couple of decades, there have been several works reported in the literature that attempt to tackle the indoor localization problem. However, most of this work is focused solely on two-dimensional (2D) localization, while very few papers consider three dimensions (3D). There is also a noticeable lack of survey papers focusing on 3D indoor localization; hence, in this paper, we aim to carry out a survey and provide a detailed critical review of the current state of the art concerning 3D indoor localization including geometric approaches such as angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), fingerprinting approaches based on Received Signal Strength (RSS), Channel State Information (CSI), Magnetic Field (MF) and Fine Time Measurement (FTM), as well as fusion-based and hybrid-positioning techniques. We provide a variety of technologies, with a focus on wireless technologies that may be utilized for 3D indoor localization such as WiFi, Bluetooth, UWB, mmWave, visible light and sound-based technologies. We critically analyze the advantages and disadvantages of each approach/technology in 3D localization.
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Machaj, Juraj, Peter Brida, and Slavomir Matuska. "Proposal for a Localization System for an IoT Ecosystem." Electronics 10, no. 23 (December 2, 2021): 3016. http://dx.doi.org/10.3390/electronics10233016.
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In the last decade, positioning using wireless signals has gained a lot of attention since it could open new opportunities for service providers. Localization is important, especially in indoor environments, where the widely used global navigation satellite systems (GNSS) signals suffer from high signal attenuation and multipath propagation, resulting in poor accuracy or a loss of positioning service. Moreover, in an Internet of things (IoT) environment, the implementation of GNSS receivers into devices may result in higher demands on battery capacity, as well as increased cost of the hardware itself. Therefore, alternative localization systems that are based on wireless signals for the communication of IoT devices are gaining a lot of attention. In this paper, we provide a design of an IoT localization system, which consists of multiple localization modules that can be utilized for the positioning of IoT devices that are connected thru various wireless technologies. The proposed system can currently perform localization based on received signals from LoRaWAN, ZigBee, Wi-Fi, UWB and cellular technologies. The implemented pedestrian dead reckoning algorithm can process the data measured by a mobile device that is equipped with inertial sensors to construct a radio map and thus help with the deployment of the positioning services based on a fingerprinting approach.
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Subedi, Santosh, and Jae-Young Pyun. "Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons." Journal of Sensors 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/9742170.
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Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultra-wideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.
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Kanan, Riad, and Obaidallah Elhassan. "A Combined Batteryless Radio and WiFi Indoor Positioning for Hospital Nursing." Journal of Communications Software and Systems 12, no. 1 (March 22, 2016): 34. http://dx.doi.org/10.24138/jcomss.v12i1.89.
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This paper proposes a design of an efficient hospital nurse calling system which combines two types of indoor localization systems. The purpose of the first system is to locate patients while the second is to locate nurses equipped with their smart phones. The main goal of developing such system is to decrease the time taking for nurses to provide healthcare for patients. Patients' positioning system is RF based. Indeed, each patient is equipped with a wireless and battery-free call button. When the switch is pressed, a wireless telegram is sent to reference nodes that act like Wireless Sensor Networks (WSN). The positioning of patient is performed using trilateration method with the help of Received Signal Strength Indicator (RSSI) values. Hence, beacons will forward the received signal from patient’s call button to a central receiver module connected to a computer. A dedicated program has been developed to calculate the position of the call button and post it on an online database. On the other hand, the nurses’ localization system is WiFi-based. Nurses' positioning is done by determining the Time of Arrival (ToA) and the Angle of Arrival (AoA) between the mobile phone and the WiFi router. The mobile phone locations are posted to the online database as well. Our program performs a comparison between the nurses' and the patient's coordinates. The nearest nurse gets an alarm. As consequence, a patient gets care from the nearest available nurse in an efficient way and with less time. The proposed system is user-friendly and Internet of Things (IoT) based architecture integrating two heterogeneous localization systems seamlessly.
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Li, Shuang, Baoguo Yu, Yi Jin, Lu Huang, Heng Zhang, and Xiaohu Liang. "Image-Based Indoor Localization Using Smartphone Camera." Wireless Communications and Mobile Computing 2021 (July 5, 2021): 1–9. http://dx.doi.org/10.1155/2021/3279059.
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With the increasing demand for location-based services such as railway stations, airports, and shopping malls, indoor positioning technology has become one of the most attractive research areas. Due to the effects of multipath propagation, wireless-based indoor localization methods such as WiFi, bluetooth, and pseudolite have difficulty achieving high precision position. In this work, we present an image-based localization approach which can get the position just by taking a picture of the surrounding environment. This paper proposes a novel approach which classifies different scenes based on deep belief networks and solves the camera position with several spatial reference points extracted from depth images by the perspective- n -point algorithm. To evaluate the performance, experiments are conducted on public data and real scenes; the result demonstrates that our approach can achieve submeter positioning accuracy. Compared with other methods, image-based indoor localization methods do not require infrastructure and have a wide range of applications that include self-driving, robot navigation, and augmented reality.
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Hatem, Elias, Sergio Fortes, Elizabeth Colin, Sara Abou-Chakra, Jean-Marc Laheurte, and Bachar El-Hassan. "Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems." Sensors 21, no. 16 (August 8, 2021): 5346. http://dx.doi.org/10.3390/s21165346.
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Indoor localization is one of the most important topics in wireless navigation systems. The large number of applications that rely on indoor positioning makes advancements in this field important. Fingerprinting is a popular technique that is widely adopted and induces many important localization approaches. Recently, fingerprinting based on mobile robots has received increasing attention. This work focuses on presenting a simple, cost-effective and accurate auto-fingerprinting method for an indoor localization system based on Radio Frequency Identification (RFID) technology and using a two-wheeled robot. With this objective, an assessment of the robot’s navigation is performed in order to investigate its displacement errors and elaborate the required corrections. The latter are integrated in our proposed localization system, which is divided into two stages. From there, the auto-fingerprinting method is implemented while modeling the tag-reader link by the Dual One Slope with Second Order propagation Model (DOSSOM) for environmental calibration, within the offline stage. During the online stage, the robot’s position is estimated by applying DOSSOM followed by multilateration. Experimental localization results show that the proposed method provides a positioning error of 1.22 m at the cumulative distribution function of 90%, while operating with only four RFID active tags and an architecture with reduced complexity.
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Maneerat, Kriangkrai, Kamol Kaemarungsi, and Chutima Prommak. "Robust Floor Determination Algorithm for Indoor Wireless Localization Systems under Reference Node Failure." Mobile Information Systems 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/4961565.
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One of the challenging problems for indoor wireless multifloor positioning systems is the presence of reference node (RN) failures, which cause the values of received signal strength (RSS) to be missed during the online positioning phase of the location fingerprinting technique. This leads to performance degradation in terms of floor accuracy, which in turn affects other localization procedures. This paper presents a robust floor determination algorithm called Robust Mean of Sum-RSS (RMoS), which can accurately determine the floor on which mobile objects are located and can work under either the fault-free scenario or the RN-failure scenarios. The proposed fault tolerance floor algorithm is based on the mean of the summation of the strongest RSSs obtained from the IEEE 802.15.4 Wireless Sensor Networks (WSNs) during the online phase. The performance of the proposed algorithm is compared with those of different floor determination algorithms in literature. The experimental results show that the proposed robust floor determination algorithm outperformed the other floor algorithms and can achieve the highest percentage of floor determination accuracy in all scenarios tested. Specifically, the proposed algorithm can achieve greater than 95% correct floor determination under the scenario in which 40% of RNs failed.
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Cheng, Long, Yong Wang, Mingkun Xue, and Yangyang Bi. "An Indoor Robust Localization Algorithm Based on Data Association Technique." Sensors 20, no. 22 (November 18, 2020): 6598. http://dx.doi.org/10.3390/s20226598.
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As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms.
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Alhammadi, Abdulraqeb, Fazirulhisyam Hashim, Mohd Fadlee A Rasid, and Saddam Alraih. "A three-dimensional pattern recognition localization system based on a Bayesian graphical model." International Journal of Distributed Sensor Networks 16, no. 9 (September 2020): 155014771988489. http://dx.doi.org/10.1177/1550147719884893.
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Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.
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Maneerat, Kriangkrai, and Kamol Kaemarungsi. "RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques." Mobile Information Systems 2019 (February 3, 2019): 1–22. http://dx.doi.org/10.1155/2019/5089626.
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Most existing wireless indoor positioning systems have only success performance requirements in normal operating situations whereby all wireless equipment works properly. There remains a lack of system reliability that can support emergency situations when there are some reference node failures, such as in earthquake and fire scenarios. Additionally, most systems do not incorporate environmental information such as temperature and relative humidity level into the process of determining the location of objects inside the building. To address these gaps, we propose a novel integrated framework for wireless indoor positioning systems based on a location fingerprinting technique which is called the Robust and low Complexity indoor positioning systems framework (RoC framework). Our proposed integrated framework consists of two essential indoor positioning processes: the system design process and the localization process. The RoC framework aims to achieve robustness in the system design structure and reliability of the target location during the online estimation phase either under a normal situation or when some reference nodes (RNs) have failed. The availability of low-cost temperature and relative humidity sensors can provide additional information for the location fingerprinting technique and thereby reduce location estimation complexity by including this additional information. Experimental results and comparative performance evaluation revealed that the RoC framework can achieve robustness in terms of the system design structure, whereby it was able to provide the highest positioning performance in either fault-free or RN-failure scenarios. Moreover, in the online estimation phase, the proposed framework can provide the highest reliability of the target location under the RN-failure scenarios and also yields the lowest computational complexity in online searching compared to other techniques. Specifically, when compared to the traditional weighted k-nearest neighbor techniques (WKNN) under the 30% RN-failure scenario at Building B, the proposed RoC framework shows 74.1% better accuracy performance and yields 55.1% lower computational time than the WKNN.
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Nawaz, Haq, Ahsen Tahir, Nauman Ahmed, Ubaid U. Fayyaz, Tayyeb Mahmood, Abdul Jaleel, Mandar Gogate, Kia Dashtipour, Usman Masud, and Qammer Abbasi. "Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models." Entropy 23, no. 11 (October 25, 2021): 1401. http://dx.doi.org/10.3390/e23111401.
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Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.
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Irshad, Liu, Arshad, Sohail, Murthy, Khokhar, and Uba. "A Novel Localization Technique Using Luminous Flux." Applied Sciences 9, no. 23 (November 21, 2019): 5027. http://dx.doi.org/10.3390/app9235027.
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As global navigation satellite system (GNNS) signals are unable to enter indoor spaces, substitute methods such as indoor localization-based visible light communication (VLC) are gaining the attention of researchers. In this paper, the systematic investigation of a VLC channel is performed for both direct and indirect line of sight (LoS) by utilizing the impulse response of indoor optical wireless channels. In order to examine the localization scenario, two light-emitting diode (LED) grid patterns are used. The received signal strength (RSS) is observed based on the positional dilution of precision (PDoP), a subset of the dilution of precision (DoP) used in global navigation satellite system (GNSS) positioning. In total, 31 × 31 possible positional tags are set for a given PDoP configuration. The values for positional error in terms of root mean square error (RMSE) and the sum of squared errors (SSE) are taken into consideration. The performance of the proposed approach is validated by simulation results according to the selected indoor space. The results show that the position accuracy enhanced is at short range by 24% by utilizing the PDoP metric. As confirmation, the modeled accuracy is compared with perceived accuracy results. This study determines the application and design of future optical wireless systems specifically for indoor localization.
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Sergi, Simone, Fabrizio Pancaldi, and Giorgio M. Vitetta. "Cluster-Based Ranging for Accurate Localization in Wireless Sensor Networks." International Journal of Navigation and Observation 2010 (July 29, 2010): 1–11. http://dx.doi.org/10.1155/2010/460860.
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A novel ranging technique based on received signal strength (RSS) and suitable to indoor scenarios is illustrated. In the proposed technique, multiple power measurements, associated with the signals radiated by a cluster of nodes surrounding a given target, are jointly processed to improve the quality of RSS-based estimation of the distance between the target and an anchor. Specific algorithms for the generation of a cluster and for the acquisition of power measurements are described. Simulation results show that, when used in indoor positioning systems, the proposed ranging technique is substantially more accurate than noncooperative strategies. In addition, it allows to concentrate significant processing tasks in a limited number of fixed anchors, so reducing maintenance costs and making it possible to adopt cheap and simple portable wireless nodes.
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Bian, Zhentian, Long Cheng, and Yan Wang. "A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment." Journal of Sensors 2021 (November 13, 2021): 1–15. http://dx.doi.org/10.1155/2021/6125890.
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While the modern communication system, embedded system, and sensor technology have been widely used at the moment, the wireless sensor network (WSN) composed of microdistributed sensors is favored due to its relatively excellent communication interaction, real-time computing, and sensing capabilities. Because GPS positioning technology cannot meet the needs of indoor positioning, positioning based on WSN has become the better option for indoor localization. In the field of WSN indoor positioning, how to cope with the impact of NLOS error on positioning is still a big problem to be solved. In order to mitigate the influence of NLOS errors, a Neural Network Modified Multiple Filter Localization (NNMML) algorithm is proposed in this paper. In this algorithm, LOS and NLOS cases are distinguished firstly. Then, KF and UKF are applied in the LOS case and the NLOS case, respectively, and appropriate grouping processing is carried out for NLOS data. Finally, the positioning results after multiple filtering are corrected by neural network. The simulation results illustrate that the location accuracy of NNMML algorithm is better than that of KF, EKF, UKF, and the version without neural network correction. It also shows that NNMML is suitable for the situation with large NLOS error.
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Chan, Eddie C. L., George Baciu, and S. C. Mak. "Properties of Channel Interference for Wi-Fi Location Fingerprinting." Journal of Communications Software and Systems 6, no. 2 (June 22, 2010): 56. http://dx.doi.org/10.24138/jcomss.v6i2.190.
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Localization systems for indoor areas have recently been suggested that make use of existing wireless local areanetwork (WLAN) infrastructure and location fingerprinting approach. However, most existing research work ignores channel interference between wireless infrastructures and this could affect accurate and precise positioning. A better understanding of the properties of channel interference could assist in improving the positioning accuracy while saving significant amounts of resources in the location-aware infrastructure. This paper investigates to what extent the positioning accuracy is affected by channel interference between access points. Two sets of experiments compare how the positioning accuracy is affected in three different channel assignment schemes: ad-hoc, sequential, and orthogonal data is analyzed to understand what features ofchannel interference affect positioning accuracy. The results show that choosing an appropriate channel assignment scheme could make localization 10% more accurate and reduces the number of access points that are required by 15%. The experimental analysis also indicates that the channel interference usually obeys a right-skewed distribution and positioning accuracy is heavily dependent on channel interference between access points (APs).
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Farheen, Roohi. "WI-FI Access Point (WAP) OPTIMAL Placement in an Indoor Location." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1084–87. http://dx.doi.org/10.22214/ijraset.2021.38128.
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Abstract: The popularity of location based applications is undiminished today. They require accurate location information which is a challenging issue in indoor environments. Wireless technologies can help derive indoor positioning data. Especially, the WiFi technology is a promising candidate due to the existing and almost ubiquitous Wi-Fi infrastructure. The already deployed WiFi devices can also serve as reference points for localization eliminating the cost of setting up a dedicated system. However, the primary purpose of these Wi-Fi systems is data communication and not providing location services. This accuracy can be increased by carefully placing the Wi-Fi access points to cover the given territory properly. This method is based on simulated annealing which finds the optimal number and placement of Wi-Fi access points with regard to indoor positioning and investigate its performance in a real environment scenario via simulations. Keywords: Wi-fi access point (WAP), simulated annealing, router, wireless, placement, locationing.
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Chen, Ching-Mu, Yung-Fa Huang, and You-Ting Jheng. "An Efficient Indoor Positioning Method with the External Distance Variation for Wireless Networks." Electronics 10, no. 16 (August 12, 2021): 1949. http://dx.doi.org/10.3390/electronics10161949.
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This study strengthens the external distance variation for the indoor positioning performance. With the received signal strength (RSS) of the unknown node, a localization is performed to positioning its coordinates. The mean square error (MSE) of localization is deteriorated by the shadowing effect and the MSE depends on the location of reference nodes. Moreover, the minimum mean square error (MMSE) algorithm is also used with the RSS. The amount of variation in the distance between the reference point and the positioning node will also affect the accuracy. Therefore, this paper considers the distance between the reference point and the positioning node and also the distance variation between the reference points. MSE is used to estimate positioning performance and Monte Carlo is also used to simulate the average error of different shadowing and decay environments. When reference nodes have known distances, the distance is obtained separately and the estimated distances are identified by the MMSE method. In order to reduce the number of reference nodes and calculation cost, this paper uses adaptive reference node selection to improve the accuracy of positioning. Simulation results show that the external distance variation mechanism strengthens the indoor positioning performance. Moreover, this paper investigates the performance of several reference nodes (three, four, five, and six reference nodes) through 3D graphs to estimate the small range area. The differences are more clearly observed with fewer reference nodes and lower MSE. Finally, simulation results show that the MSE value of fixed three reference nodes is almost 100% better with external distance variation method compared to the random selected three reference nodes.
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Mahdi, Alaa A., Abdolah Chalechale, and Ashraf AbdelRaouf. "A Hybrid Indoor Positioning Model for Critical Situations Based on Localization Technologies." Mobile Information Systems 2022 (April 13, 2022): 1–15. http://dx.doi.org/10.1155/2022/8033380.
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The domains of positioning and tracking have undergone substantial evolution and advancements recently, especially within the concept of the Internet of Things (IoT) and in health care. Unfortunately, neither the current satellite positioning systems nor the standalone cellular systems remain useful for successfully localizing and tracking inside buildings. This paper proposes a new model that could improve the accuracy of localization in indoor environments. In addition, a broad review is conducted to discover the state-of-the-art indoor localization technologies appropriate for disasters and rescue situations. After a comprehensive study, three important technologies that need to be deeply reviewed are identified, which are wireless local area network (WLAN), dead reckoning (DR), and hybrid approaches. Based on these, a novel architecture is introduced that is more convenient to meet the operation of rescuing injured or older people in critical situations, where other technologies might be unavailable or require some extra infrastructures. The proposed model has two modes and selects one of these modes automatically. The first mode assumes the existence of both WLAN signals and smartphone sensors to be used for identifying the position of the object; otherwise, only smartphone sensors will be employed to achieve positioning. Significantly, the designated components and the flow control depicted provide a proper and suitable horizon for the next researchers who desire to develop a new indoor positioning system in this discipline with a low positioning root-mean-squared error on the centimeter scale that can later be incorporated in numerous applications relating to the IoT, health care, and evacuation plans.
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Xiang, Peng, Peng Ji, and Dian Zhang. "Enhance RSS-Based Indoor Localization Accuracy by Leveraging Environmental Physical Features." Wireless Communications and Mobile Computing 2018 (July 9, 2018): 1–8. http://dx.doi.org/10.1155/2018/8956757.
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Indoor localization technologies based on Radio Signal Strength (RSS) attract many researchers’ attentions, since RSS can be easily obtained by wireless devices without additional hardware. However, such technologies are apt to be affected by indoor environments and multipath phenomenon. Thus, the accuracy is very difficult to improve. In this paper, we put forward a method, which is able to leverage various other resources in localization. Besides the traditional RSS information, the environmental physical features, e.g., the light, temperature, and humidity information, are all utilized for localization. After building a comprehensive fingerprint map for the above information, we propose an algorithm to localize the target based on Naïve Bayesian. Experimental results show that the successful positioning accuracy can dramatically outperform traditional pure RSS-based indoor localization method by about 39%. Our method has the potential to improve all the radio frequency (RF) based localization approaches.
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Kianfar, Amir Ehsan, Fabian Uth, Ralph Baltes, and Elisabeth Clausen. "Development of a Robust Ultra-Wideband Module for Underground Positioning and Collision Avoidance." Mining, Metallurgy & Exploration 37, no. 6 (July 31, 2020): 1821–25. http://dx.doi.org/10.1007/s42461-020-00279-6.
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AbstractAs indoor positioning provides particular challenges due to the unavailability of GPS signals, various systems such as ultra-wideband (UWB), radio frequency identification (RFID), ultrasound, and wireless local area network (WLAN) have been proposed in recent years. Some of these technologies are currently being marketed and some are still being developed. UWB technology allows for higher precision while also reducing power consumption. Hence, the underground automation and localization systems can use this technology for more accuracy and robustness. This article discusses new robust UWB modules used for underground positioning and collision avoidance with regard to human safety in underground mining operations.
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Cheng, Long, Mingkun Xue, Ze Liu, and Yong Wang. "A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network." Applied Sciences 10, no. 1 (December 18, 2019): 6. http://dx.doi.org/10.3390/app10010006.
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As one of the core technologies of the Internet of Things, wireless sensor network technology is widely used in indoor localization systems. Considering that sensors can be deployed to non-line-of-sight (NLOS) environments to collect information, wireless sensor network technology is used to locate positions in complex NLOS environments to meet the growing positioning needs of people. In this paper, we propose a novel time of arrival (TOA)-based localization scheme. We regard the line-of-sight (LOS) environment and non-line-of-sight environment in wireless positioning as a Markov process with two interactive models. In the NLOS model, we propose a modified probabilistic data association (MPDA) algorithm to reduce the NLOS errors in position estimation. After the NLOS recognition, if the number of correct positions is zero continuously, it will lead to inaccurate localization. In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve a higher localization accuracy when compared with the existing methods.
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YANG, CHI-LU, YEIM-KUAN CHANG, YU-TSO CHEN, CHIH-PING CHU, and CHI-CHANG CHEN. "A SELF-ADAPTABLE INDOOR LOCALIZATION SCHEME FOR WIRELESS SENSOR NETWORKS." International Journal of Software Engineering and Knowledge Engineering 21, no. 01 (February 2011): 33–54. http://dx.doi.org/10.1142/s0218194011005153.
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Service systems used for various applications in home automation and security require estimating the locations precisely using certain sensors. Serving a mobile user automatically by sensing his/her locations in an indoor environment is considered as a challenge. However, indoor localization cannot be carried out effectively using the Global Positioning System (GPS). In recent years, the use of Wireless Sensor Networks (WSNs) in locating a mobile object in an indoor environment has become popular. Some physical features have also been discussed to solve localization in WSNs. In this paper, we inquire into received signal strength indication (RSSI)-based solutions and propose a new localization scheme called the closer tracking algorithm (CTA) for indoor localization. Under the proposed CTA, a mechanism on mode-change is designed to switch automatically between the optimal approximately closer approach (ACA) and the real-time tracking (RTT) method according to pre-tuned thresholds. Furthermore, we design a mechanism to move reference nodes dynamically to reduce the uncovered area of the ACA for increasing the estimation accuracy. We evaluate the proposed CTA using ZigBee CC2431 modules. The experimental results show that the proposed CTA can determine the position accurately with an error distance less than 0.9 m. At the same time, the CTA scheme has at least 87% precision when the distance is less than 0.9 m. The proposed CTA can select an adaptive mode properly to improve the localization accuracy with high confidence. Moreover, the experimental results also show that the accuracy can be improved by the deployment and movement of reference nodes.
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Cheng, Fang, Guofeng Niu, Zhizhong Zhang, and Chengjie Hou. "Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm." Sensors 22, no. 23 (December 6, 2022): 9531. http://dx.doi.org/10.3390/s22239531.
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With the intense deployment of wireless systems and the widespread use of intelligent equipment, the requirement for indoor positioning services is increasing, and Wi-Fi fingerprinting has emerged as the most often used approach to identifying indoor target users. The construction time of the Wi-Fi received signal strength (RSS) fingerprint database is short, but the positioning performance is unstable and susceptible to noise. Meanwhile, to strengthen indoor positioning precision, a fingerprints algorithm based on a convolution neural network (CNN) is often used. However, the number of reference points participating in the location estimation has a great influence on the positioning accuracy. There is no standard for the number of reference points involved in position estimation by traditional methods. For the above problems, the grayscale images corresponding to RSS and angle of arrival are fused into RGB images to improve stability. This paper presents a position estimation method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm, which can select appropriate reference points according to the situation. DBSCAN analyses the CNN output and can choose the number of reference points based on the situation. Finally, the position is approximated using the weighted k-nearest neighbors. The results show that the calculation error of our proposed method is at least 0.1–0.3 m less than that of the traditional method.
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Long, Keliu, Darryl Franck Nsalo Kong, Kun Zhang, Chuan Tian, and Chong Shen. "A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction." Sensors 21, no. 19 (September 27, 2021): 6447. http://dx.doi.org/10.3390/s21196447.
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A fingerprint-based localization system is an economic way to solve an indoor positioning problem. However, the traditional off-line fingerprint collection stage is a time-consuming and laborious process which limits the use of fingerprint-based localization systems. In this paper, based on ubiquitous Wireless Fidelity (Wi-Fi) equipment and a low-cost Ultra-Wideband (UWB) ranging system (with only one UWB anchor), a ready-to-use indoor localization system is proposed to realize long-term and high-accuracy indoor positioning. More specifically, in this system, it is divided into two stages: (1) an initial stage, and (2) a positioning stage. In the initial stage, an Inertial Measure Unit (IMU) is used to calculate the position using Pedestrian Dead Reckon (PDR) algorithm within a preset number of steps, and the location-related fingerprints are collected to train a Convolutional Neural Network (CNN) regression model; simultaneously, in order to make the UWB ranging system adapt to the Non-Line-of-Sight (NLoS) environment, the increments of acceleration and angular velocity in IMU and the increments of single UWB ranging measures are correlated to pre-train a Supported Vector Regression (SVR). After reaching the threshold of time or step number, the system is changed into a positioning stage, and the CNN predicts the position calibrated by corrected UWB ranging. At last, a series of practical experiments are conducted in the real environment; the experiment results show that, due to the corrected UWB ranging measures calibrating the CNN parameters in every positioning period, this system has stable localization results in a comparative long-term range. Additionally, it has the advantages of stability, low cost, anti-noise, etc.
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Wang, Yanzhao, Chundi Xiu, Xuanli Zhang, and Dongkai Yang. "WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest." Sensors 18, no. 9 (August 31, 2018): 2869. http://dx.doi.org/10.3390/s18092869.
Full textAbstract:
WiFi fingerprinting indoor positioning systems have extensive applied prospects. However, a vast amount of data in a particular environment has to be gathered to establish a fingerprinting database. Deficiencies of these systems are the lack of universality of multipath effects and a burden of heavy workload on fingerprint storage. Thus, this paper presents a novel Random Forest fingerprinting localization (RFFP) method using channel state information (CSI), which utilizes the Random Forest model trained in the offline stage as fingerprints in order to economize memory space and possess a good anti-multipath characteristic. Furthermore, a series of specific experiments are conducted in a microwave anechoic chamber and an office to detail the localization performance of RFFP with different wireless channel circumstances, system parameters, algorithms, and input datasets. In addition, compared with other algorithms including K-Nearest-Neighbor (KNN), Weighted K-Nearest-Neighbor (WKNN), REPTree, CART, and J48, the RFFP method provides far greater classification accuracy as well as lower mean location error. The proposed method offers outstanding comprehensive performance including accuracy, robustness, low workload, and better anti-multipath-fading.
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Bruno, Luigi, Paolo Addesso, and Rocco Restaino. "Indoor Positioning in Wireless Local Area Networks with Online Path-Loss Parameter Estimation." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/986714.
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Location based services are gathering an even wider interest also in indoor environments and urban canyons, where satellite systems like GPS are no longer accurate. A much addressed solution for estimating the user position exploits the received signal strengths (RSS) in wireless local area networks (WLANs), which are very common nowadays. However, the performances of RSS based location systems are still unsatisfactory for many applications, due to the difficult modeling of the propagation channel, whose features are affected by severe changes. In this paper we propose a localization algorithm which takes into account the nonstationarity of the working conditions by estimating and tracking the key parameters of RSS propagation. It is based on a Sequential Monte Carlo realization of the optimal Bayesian estimation scheme, whose functioning is improved by exploiting the Rao-Blackwellization rationale. Two key statistical models for RSS characterization are deeply analyzed, by presenting effective implementations of the proposed scheme and by assessing the positioning accuracy by extensive computer experiments. Many different working conditions are analyzed by simulated data and corroborated through the validation in a real world scenario.
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Ahmadi, Hanen, and Ridha Bouallegue. "An accurate target tracking method in wireless sensor networks." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (March 1, 2022): 1589. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1589-1598.
Full textAbstract:
<span>Localization is a challenging research issue in various sectors of activity, particularly in dynamic indoor environment with high perturbation. Many existing localization techniques in wireless sensor networks are not efficient because of many constraints such as the high cost, the memory and energy limitation and the environmental noise effects. Thus, the development of novel methods of localization has become a great concern for the wireless sensor networks. In this paper, we propose a tracking method that combines regression tree and Kalman smoother filtering. Previously, regression tree has been suggested for static positioning by means of received signal strength indicator measurements. In this work, we employ this strategy to solve the mapping relation between these measurements and the target position by means of an optimized propagation model. Moreover, the predicted position considered as the observed information is introduced to the Kalman smoother algorithm, to have more precise state of the moving target. The proposed algorithm has been assessed and compared to other existing methods using real measurements of the received power by the moving target in an indoor environment. The evaluation shows that our solution outperforms other methods regarding localization accuracy.</span>
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Bragin, D. S., I. V. Pospelova, I. V. Cherepanova, and V. N. Serebryakova. "Radiofrequency Technologies of Local Positioning in Healthcare." Journal of the Russian Universities. Radioelectronics 23, no. 3 (July 21, 2020): 62–79. http://dx.doi.org/10.32603/1993-8985-2020-23-3-62-79.
Full textAbstract:
Introduction. Localization of objects position in closed space plays an important role in many areas of human activity, including medicine. Using indoor-positioning technologies as a part of telemedicine systems allows one to improve the quality of medical care and to reduce mortality of patients. Therefore, indoor-positioning technologies contribute to achieve the goals outlined in the Russian Federation government`s program "Healthcare development". Aim. To study the applicability of modern radiofrequency technologies for localization of patients inside a hospital building. Materials and methods. Scientific sources devoted to indoor-positioning based on radiofrequency technologies were analyzed. The methods used included: - bibliographic retrieval; - selection and verification of sources based on their relevance; - analysis of sources by methods of deconstruction and comparative analysis . Results. The result of the analysis indicated that radiofrequency positioning technologies allow one to locate objects using radio waves properties. The disadvantage of the technology is the penetration of radio signal through walls and floors. Given this, it is necessary to use complex algorithms to detect an object with accuracy to a specific room. Despite this disadvantage, radiofrequency technologies can be used for positioning in medical facilities since they are easy in deployment and service. Also, they are used in ready-made commercial solutions. ZigBee technology is an exception because it does not allow one to track moving objects in real-time. Conclusion. Based on the study it was concluded that BLE technology is the most suitable for indoor-positioning in medical facilities. It is energy-efficient, it has sufficiently fast data transfer rate, good communication radius and a large range of ready-made communication equipment. It is also worth noting that most wireless medical sensors exchange data via the BLE interface.
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Javed, Iram, Xianlun Tang, Muhammad Asim Saleem, Muhammad Umer Sarwar, Maham Tariq, and Casper Shikali Shivachi. "3D Localization for Mobile Node in Wireless Sensor Network." Wireless Communications and Mobile Computing 2022 (March 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/3271265.
Full textAbstract:
Wireless sensor network (WSN) is an emerging technology that can detect, collect, and transmit information in a specific unknown area in an unknown environment. It is currently playing an increasingly important role in the fields of national defense, medical and health, and daily life. WSN node location information is extremely important in many WSN applications. The data information collected by WSN is developed based on known node location information. The node location is one of the important issues in WSNs. Location information is very important for wireless sensors. A WSN without sensor node location information is meaningless because almost all WSN applications need to know node location information, such as animal populations, tracking research, early warning of building fires, management of goods in warehouses, and traffic monitoring systems. Several research works are underway to expand the 2D positioning algorithm in WSN to 3D regardless of the deployment structure of sensor nodes. This paper proposes an improved Savarese algorithm to the problem of singularity in WSN node localization. The proposed algorithm is a modified version of the conventional Savarese algorithm, and it solves the singularity problem and improved the positioning accuracy. Simulation results show that the proposed algorithm effectively improved system performance, and the accuracy is improved over 2.83% and 2.96% than the existing algorithms. The proposed scheme is effective for indoor environments while it can be deployed outdoor for small-scale.