Journal articles: 'Indoor localization'

1

Rohmat Rose, Nur Diana, and Low Tan Jung. "Comparison of Indoor Localization Scheme for Multistory Environment." Advanced Journal of Technical and Vocational Education 4, no. 3 (September 30, 2020): 8–13. http://dx.doi.org/10.26666/rmp.sjtve.2020.3.2.

Full text

Abstract:

Outdoor localization nowadays is a part of our daily life. However, such useful localization method as the Global Positioning System (GPS) fails inside buildings. Popular alternative is to utilize the Indoor Positioning System (IPS) technologies that are accessible indoors. There is a wide range of IPS technologies that can be used within an indoor environment. Moreover, more attention has been given to IPS for multistory buildings recently. In indoor localization, different techniques and methods are used for distance and position estimation. This paper will focus on indoor localization technologies and the comparison of indoor localization methods focusing on multistory environment which will improve the localization accuracy through various models and techniques.

APA, Harvard, Vancouver, ISO, and other styles

2

Abkari, Safae El. "Wireless Indoor Localization Using Fingerprinting Technique." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 2597–602. http://dx.doi.org/10.5373/jardcs/v12sp7/20202394.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Varshavsky, Alex, Eyal de Lara, Jeffrey Hightower, Anthony LaMarca, and Veljo Otsason. "GSM indoor localization." Pervasive and Mobile Computing 3, no. 6 (December 2007): 698–720. http://dx.doi.org/10.1016/j.pmcj.2007.07.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Azrad, Syaril, Mohammad Fadhil, Farid Kendoul, and Kenzo Nonami. "Quadrotor UAV Indoor Localization Using Embedded Stereo Camera." Applied Mechanics and Materials 629 (October 2014): 270–77. http://dx.doi.org/10.4028/www.scientific.net/amm.629.270.

Full text

Abstract:

Localization of Small-Size Unmanned Air Vehicles (UAVs) such as the Quadrotors inGlobal Positioning System (GPS)-denied environment such as indoors has been done using varioustechniques. Most of the experiment indoors that requires localization of UAVs, used cameras or ultrasonicsensors installed indoor or applied indoor environment modification such as patching (InfraRed) IR and visual markers. While these systems have high accuracy for the UAV localization, theyare expensive and have less practicality in real situations. We propose a system consisting of a stereocamera embedded on a quadrotor UAV for indoor localization. The optical flow data from the stereocamera then are fused with attitude and acceleration data from our sensors to get better estimationof the quadrotor location. Using stereo camera capabilities the quadrotor altitude are estimated usingSIFT Feature Stereo Matching are used in addition to the altitude estimation computed using opticalflow. To avoid latency due to computational time, image processing and the quadrotor control areprocessed threads and core allocation.

APA, Harvard, Vancouver, ISO, and other styles

5

Ingabire, Winfred, Hadi Larijani, Ryan M. Gibson, and Ayyaz-UI-Haq Qureshi. "LoRaWAN Based Indoor Localization Using Random Neural Networks." Information 13, no. 6 (June 16, 2022): 303. http://dx.doi.org/10.3390/info13060303.

Full text

Abstract:

Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN) RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor car parks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%.

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

7

Weiping Zhu, Weiping Zhu, and Xiaoling Cheng Weiping Zhu. "Indoor Localization Method of Mobile Educational Robot Based on Visual Sensor." 網際網路技術學刊 24, no. 1 (January 2023): 205–15. http://dx.doi.org/10.53106/160792642023012401019.

Full text

Abstract:

<p>This article aims to study the mobile positioning method of mobile educational robots indoors. In order for robots to be able to unblocked indoors, they can avoid obstacles well. Vision sensors are the direct source of information for the entire machine vision system, and are mainly composed of one or two graphics sensors, sometimes accompanied by light projectors and other auxiliary equipment. This paper presents an indoor positioning method for mobile educational robots based on visual sensors. Build some models to compare which algorithm is more in line with the positioning of indoor mobile educational robots. The experimental results in this paper show that the positioning accuracy of the optical flow meter and the odometer on the short-haired carpet is equivalent (both are less than the index 4.52%); the positioning error of the optical flowmeter on the long-haired carpet is the largest 7%, and the positioning error of the odometer is the largest it reached 83%. The error of the algorithm positioning method after the visual odometer fusion is obviously smaller than that of the optical flow method. This shows that the algorithm after visual process fusion is more suitable for indoor mobile educational robot positioning than this optical flow method.</p> <p> </p>

APA, Harvard, Vancouver, ISO, and other styles

8

Cheon, Sooyoung, Daekug Lee, and Ah-Rim Joo. "Machine Learning Indoor Localization Study Based on RSSI Data." Korean Data Analysis Society 25, no. 6 (December 31, 2023): 2159–70. http://dx.doi.org/10.37727/jkdas.2023.25.6.2159.

Full text

Abstract:

With the recent development of science and technology, localization technology has made it possible to provide customized mobile services based on real-time location information to users through smartphones and Internet of Things devices. In particular, GPS-based real-time localization is commonly used. However, unlike outdoors, not only does the GPS reception rate decrease significantly indoors, but it is also difficult to accurately measure the user's indoor location using only GPS latitude and longitude information. In this study, we propose to measure the user's indoor location using RSSI (received signal strength indicator). For this purpose, an indoor localization study was conducted by applying machine learning classifiers, SVM, decision tree, ExtraTrees, random forest, and KNN to RSSI data. Among the machine learning classifiers, random forest exhibited the best performance. Therefore, we applied random forest-based RFE to extract features from RSSI data. We confirmed that even with a smaller amount of data, it was possible to achieve more accurate indoor positioning. In addition, it was confirmed through regression analysis that latitude and longitude can be estimated from RSSI data, so that indoor as well as outdoor locations can be estimated.

APA, Harvard, Vancouver, ISO, and other styles

9

Yan, Kun, Hsiao-Chun Wu, Shih-Hau Fang, Chiapin Wang, Shaopeng Li, and Lixuan Zhang. "Indoor Femtocell Interference Localization." IEEE Transactions on Wireless Communications 19, no. 8 (August 2020): 5176–87. http://dx.doi.org/10.1109/twc.2020.2990228.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Lymberopoulos, Dimitrios, Jie Liu, Xue Yang, Romit Roy Choudhury, Souvik Sen, and Vlado Handziski. "Microsoft Indoor Localization Competition." GetMobile: Mobile Computing and Communications 18, no. 4 (January 14, 2015): 24–31. http://dx.doi.org/10.1145/2721914.2721923.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Wu, Kaishun, Jiang Xiao, Youwen Yi, Dihu Chen, Xiaonan Luo, and Lionel M. Ni. "CSI-Based Indoor Localization." IEEE Transactions on Parallel and Distributed Systems 24, no. 7 (July 2013): 1300–1309. http://dx.doi.org/10.1109/tpds.2012.214.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Kim, Cheong-Mi, and Beakcheol Jang. "Indoor Localization Technology Survey." Journal of the Korea Society of Computer and Information 21, no. 1 (January 30, 2016): 17–24. http://dx.doi.org/10.9708/jksci.2016.21.1.017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Papale, Leonardo, Alexandro Catini, Rosamaria Capuano, Valerio Allegra, Eugenio Martinelli, Massimo Palmacci, Giovanna Tranfo, and Corrado Di Natale. "Personal VOCs Exposure with a Sensor Network Based on Low-Cost Gas Sensor, and Machine Learning Enabled Indoor Localization." Sensors 23, no. 5 (February 23, 2023): 2457. http://dx.doi.org/10.3390/s23052457.

Full text

Abstract:

Indoor locations with limited air exchange can easily be contaminated by harmful volatile compounds. Thus, is of great interest to monitor the distribution of chemicals indoors to reduce associated risks. To this end, we introduce a monitoring system based on a Machine Learning approach that processes the information delivered by a low-cost wearable VOC sensor incorporated in a Wireless Sensor Network (WSN). The WSN includes fixed anchor nodes necessary for the localization of mobile devices. The localization of mobile sensor units is the main challenge for indoor applications. Yes. The localization of mobile devices was performed by analyzing the RSSIs with machine learning algorithms aimed at localizing the emitting source in a predefined map. Tests performed on a 120 m2 meandered indoor location showed a localization accuracy greater than 99%. The WSN, equipped with a commercial metal oxide semiconductor gas sensor, was used to map the distribution of ethanol from a point-like source. The sensor signal correlated with the actual ethanol concentration as measured by a PhotoIonization Detector (PID), demonstrating the simultaneous detection and localization of the VOC source.

APA, Harvard, Vancouver, ISO, and other styles

14

Fang, Xuming, and Lijun Chen. "An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks." Sensors 20, no. 6 (March 24, 2020): 1798. http://dx.doi.org/10.3390/s20061798.

Full text

Abstract:

The Global Positioning System (GPS) is unable to provide precise localization services indoors, which has led to wireless sensor network (WSN) localization technology becoming a hot research issue in the field of indoor location. At present, the ranging technology of wireless sensor networks based on received signal strength has been extensively used in indoor positioning. However, wireless signals have serious multipath effects in indoor environments. In order to reduce the adverse influence of multipath effects on distance estimation between nodes, a multi-channel ranging localization algorithm based on signal diversity is herein proposed. In real indoor environments, the parameters used for multi-channel localization algorithms are generally unknown or time-varying. In order to increase the positioning accuracy of the multi-channel location algorithm in a multipath environment, we propose an optimal multi-channel trilateration positioning algorithm (OMCT) by establishing a novel multi-objective evolutionary model. The presented algorithm utilizes a three-edge constraint to prevent the traditional multi-channel localization algorithm falling into local optima. The results of a large number of practical experiments and numerical simulations show that no matter how the channel number and multipath number change, the positioning error of our presented algorithm is always smaller compared with that of the state-of-the-art algorithm.

APA, Harvard, Vancouver, ISO, and other styles

15

Gufran, Danish, and Sudeep Pasricha. "FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices." ACM Transactions on Embedded Computing Systems 22, no. 5s (September 9, 2023): 1–24. http://dx.doi.org/10.1145/3607919.

Full text

Abstract:

Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments. However, achieving accurate indoor localization can be challenging due to heterogeneity in the hardware and software stacks of mobile devices, which can result in inconsistent and inaccurate location estimates. Traditional ML models also heavily rely on initial training data, making them vulnerable to degradation in performance with dynamic changes across indoor environments. To address the challenges due to device heterogeneity and lack of adaptivity, we propose a novel embedded ML framework called FedHIL . Our framework combines indoor localization and federated learning (FL) to improve indoor localization accuracy in device-heterogeneous environments while also preserving user data privacy. FedHIL integrates a domain-specific selective weight adjustment approach to preserve the ML model's performance for indoor localization during FL, even in the presence of extremely noisy data. Experimental evaluations in diverse real-world indoor environments and with heterogeneous mobile devices show that FedHIL outperforms state-of-the-art FL and non-FL indoor localization frameworks. FedHIL is able to achieve 1.62 × better localization accuracy on average than the best performing FL-based indoor localization framework from prior work.

APA, Harvard, Vancouver, ISO, and other styles

16

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

17

Zhu, Hui, Li Cheng, Xuan Li, and Haiwen Yuan. "Neural-Network-Based Localization Method for Wi-Fi Fingerprint Indoor Localization." Sensors 23, no. 15 (August 7, 2023): 6992. http://dx.doi.org/10.3390/s23156992.

Full text

Abstract:

Despite the high demand for Internet location service applications, Wi-Fi indoor localization often suffers from time- and labor-intensive data collection processes. This study proposes a novel indoor localization model that utilizes fingerprinting technology based on a convolutional neural network to address this issue. The aim is to enhance Wi-Fi indoor localization by streamlining the data collection process. The proposed indoor localization model leverages a 3D ray-tracing technique to simulate the wireless received signal strength intensity (RSSI) across the field. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of Wi-Fi indoor localization. In addition, an RSSI heatmap fingerprint dataset generated from the ray-tracing simulation is trained on the proposed indoor localization model. To optimize and evaluate the model’s performance in real-world scenarios, experiments were conducted using simulated datasets obtained from the publicly available databases of UJIIndoorLoc and Wireless InSite. The results show that the new approach solves the problem of resource limitation while achieving a verification accuracy of up to 99.09%.

APA, Harvard, Vancouver, ISO, and other styles

18

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

19

Naser, Rana Sabah, Meng Chun Lam, Faizan Qamar, and B. B. Zaidan. "Smartphone-Based Indoor Localization Systems: A Systematic Literature Review." Electronics 12, no. 8 (April 11, 2023): 1814. http://dx.doi.org/10.3390/electronics12081814.

Full text

Abstract:

These recent years have witnessed the importance of indoor localization and tracking as people are spending more time indoors, which facilitates determining the location of an object. Indoor localization enables accurate and reliable location-based services and navigation within buildings, where GPS signals are often weak or unavailable. With the rapid progress of smartphones and their growing usage, smartphone-based positioning systems are applied in multiple applications. The smartphone is embedded with an inertial measurement unit (IMU) that consists of various sensors to determine the walking pattern of the user and form a pedestrian dead reckoning (PDR) algorithm for indoor navigation. As such, this study reviewed the literature on indoor localization based on smartphones. Articles published from 2015 to 2022 were retrieved from four databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. In total, 109 articles were reviewed from the 4186 identified based on inclusion and exclusion criteria. This study unveiled the technology and methods utilized to develop indoor localization systems. Analyses on sample size, walking patterns, phone poses, and sensor types reported in previous studies are disclosed in this study. Next, academic challenges, motivations, and recommendations for future research endeavors are discussed. Essentially, this systematic literature review (SLR) highlights the present research overview. The gaps identified from the SLR may assist future researchers in planning their research work to bridge those gaps.

APA, Harvard, Vancouver, ISO, and other styles

20

Md Din, Marina, Norziana Jamil, Jacentha Maniam, and Mohamad Afendee Mohamed. "Review of indoor localization techniques." International Journal of Engineering & Technology 7, no. 2.14 (April 6, 2018): 201. http://dx.doi.org/10.14419/ijet.v7i2.14.12980.

Full text

Abstract:

Global Positioning System (GPS) has practically solved the problem of outdoor localization. However, limitation of GPS leads to a challenge for developing a new tracking system for indoor environment. Hence, the demand for accurate indoor localization services has become important. Until now, researches related to IPS are still being conducted with the objective to improve the performance of positioning techniques. This paper provides a comprehensive review of indoor localization techniques and stimulate new research effort in this field. Current existing indoor localization system that used for tracking objects were reviewed along with some further discussion to design a better indoor localization technique.

APA, Harvard, Vancouver, ISO, and other styles

21

Kim, Yungeun, Songhee Lee, Seokjoon Lee, and Hojung Cha. "A GPS Sensing Strategy for Accurate and Energy-Efficient Outdoor-to-Indoor Handover in Seamless Localization Systems." Mobile Information Systems 8, no. 4 (2012): 315–32. http://dx.doi.org/10.1155/2012/109129.

Full text

Abstract:

Indoor localization systems typically locate users on their own local coordinates, while outdoor localization systems use global coordinates. To achieve seamless localization from outdoors to indoors, a handover technique that accurately provides a starting position to the indoor localization system is needed. However, existing schemes assume that a starting position is known a priori or uses a naïve approach to consider the last location obtained from GPS as the handover point. In this paper, we propose an accurate handover scheme that monitors the signal-to-noise ratio (SNR) of the effective GPS satellites that are selected according to their altitude. We also propose an energy-efficient handover mechanism that reduces the GPS sampling interval gradually. Accuracy and energy efficiency are experimentally validated with the GPS logs obtained in real life.

APA, Harvard, Vancouver, ISO, and other styles

22

Yu, Yue, Yi Zhang, Liang Chen, and Ruizhi Chen. "Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization." Remote Sensing 15, no. 5 (February 22, 2023): 1202. http://dx.doi.org/10.3390/rs15051202.

Full text

Abstract:

Due to the complexity of urban environments, localizing pedestrians indoors using mobile terminals is an urgent task in many emerging areas. Multi-source fusion-based localization is considered to be an effective way to provide location-based services in large-scale indoor areas. This paper presents an intelligent 3D indoor localization framework that uses the integration of Wi-Fi, Bluetooth Low Energy (BLE), quick response (QR) code, and micro-electro-mechanical system sensors (the 3D-WBQM framework). An enhanced inertial odometry was developed for accurate pedestrian localization and trajectory optimization in indoor spaces containing magnetic interference and external acceleration, and Wi-Fi fine time Measurement stations, BLE nodes, and QR codes were applied for landmark detection to provide an absolute reference for trajectory optimization and crowdsourced navigation database construction. In addition, the robust unscented Kalman filter (RUKF) was applied as a generic integrated model to combine the estimated location results from inertial odometry, BLE, QR, Wi-Fi FTM, and the crowdsourced Wi-Fi fingerprinting for large-scale indoor positioning. The experimental results indicated that the proposed 3D-WBQM framework was verified to realize autonomous and accurate positioning performance in large-scale indoor areas using different location sources, and meter-level positioning accuracy can be acquired in Wi-Fi FTM supported areas.

APA, Harvard, Vancouver, ISO, and other styles

23

Han, Chong, Wenjing Xun, Lijuan Sun, Zhaoxiao Lin, and Jian Guo. "DSCP: Depthwise Separable Convolution-Based Passive Indoor Localization Using CSI Fingerprint." Wireless Communications and Mobile Computing 2021 (January 3, 2021): 1–17. http://dx.doi.org/10.1155/2021/8821129.

Full text

Abstract:

Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.

APA, Harvard, Vancouver, ISO, and other styles

24

Yu, Wen Bin, Peng Li, Zhi Chen, and Chang Li. "PDR-Aided Algorithm with WiFi Fingerprint Matching for Indoor Localization." Applied Mechanics and Materials 701-702 (December 2014): 989–93. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.989.

Full text

Abstract:

Recently, indoor localization is essential to enable location-based services for many mobile and social network applications. Due to fluctuation of the wireless signal, the accuracy of a simple WiFi fingerprint-based localization is not high. In this paper, we first exploit Pedestrian Dead Reckoning (PDR) technology to overcome the problem of the wireless signal fluctuation, then propose a PDR-aided algorithm with WiFi fingerprint matching for indoor localization, which using the PDR technology aided indoor localization. Experiments show that our algorithm has better accuracy than other indoor localization methods.

APA, Harvard, Vancouver, ISO, and other styles

25

Yoon, Jeonghyeon, and Seungku Kim. "Practical and Accurate Indoor Localization System Using Deep Learning." Sensors 22, no. 18 (September 7, 2022): 6764. http://dx.doi.org/10.3390/s22186764.

Full text

Abstract:

Indoor localization is an important technology for providing various location-based services to smartphones. Among the various indoor localization technologies, pedestrian dead reckoning using inertial measurement units is a simple and highly practical solution for indoor localization. In this study, we propose a smartphone-based indoor localization system using pedestrian dead reckoning. To create a deep learning model for estimating the moving speed, accelerometer data and GPS values were used as input data and data labels, respectively. This is a practical solution compared with conventional indoor localization mechanisms using deep learning. We improved the positioning accuracy via data preprocessing, data augmentation, deep learning modeling, and correction of heading direction. In a horseshoe-shaped indoor building of 240 m in length, the experimental results show a distance error of approximately 3 to 5 m.

APA, Harvard, Vancouver, ISO, and other styles

26

Liu, Yimin, Wenyan Liu, and Xiangyang Luo. "Survey on the Indoor Localization Technique of Wi-Fi Access Points." International Journal of Digital Crime and Forensics 10, no. 3 (July 2018): 27–42. http://dx.doi.org/10.4018/ijdcf.2018070103.

Full text

Abstract:

This article describes how indoor localization of Wi-Fi AP (access point) plays an important role in the discovery of illegal indoor Wi-Fi and for the safety inspection of confidential places. There have been many related research results in recent years. In this article, a review is presented on the indoor localization technique of Wi-Fi AP. First, indoor localization methods of Wi-Fi AP can be divided into three categories: localization based on signal strength; fingerprint feature; and distance measurement. Then, the basic principles of the three methods are described respectively, and an evaluation of the typical methods are provided. Finally, the authors point out some research tendency of the indoor localization techniques of Wi-Fi AP.

APA, Harvard, Vancouver, ISO, and other styles

27

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

28

Wu, Zhefu, Lei Jiang, Zhuangzhuang Jiang, Bin Chen, Kai Liu, Qi Xuan, and Yun Xiang. "Accurate Indoor Localization Based on CSI and Visibility Graph." Sensors 18, no. 8 (August 3, 2018): 2549. http://dx.doi.org/10.3390/s18082549.

Full text

Abstract:

Passive indoor localization techniques can have many important applications. They are nonintrusive and do not require users carrying measuring devices. Therefore, indoor localization techniques are widely used in many critical areas, such as security, logistics, healthcare, etc. However, because of the unpredictable indoor environment dynamics, the existing nonintrusive indoor localization techniques can be quite inaccurate, which greatly limits their real-world applications. To address those problems, in this work, we develop a channel state information (CSI) based indoor localization technique. Unlike the existing methods, we employ both the intra-subcarrier statistics features and the inter-subcarrier network features. Specifically, we make the following contributions: (1) we design a novel passive indoor localization algorithm which combines the statistics and network features; (2) we modify the visibility graph (VG) technique to build complex networks for the indoor localization applications; and (3) we demonstrate the effectiveness of our technique using real-world deployments. The experimental results show that our technique can achieve about 96% accuracy on average and is more than 9% better than the state-of-the-art techniques.

APA, Harvard, Vancouver, ISO, and other styles

29

Zuo, Xing. "Advances in Indoor Localization: Comparative Study of RFID, Wi-Fi, and Visible Light Methods." Highlights in Science, Engineering and Technology 62 (July 27, 2023): 47–53. http://dx.doi.org/10.54097/hset.v62i.10423.

Full text

Abstract:

Due to the limitation that the global positioning system cannot be used in indoor settings and the increasing demand for location-based services in indoor environments, indoor localization has become one of the hottest research topics in recent years. A variety of techniques have been proposed for indoor localization. This review paper aims to provide an overview of working principles of radio frequency identification based, Wi-Fi based and visible light localization techniques and their applications. The paper also presents a comprehensive analysis of the advantages and disadvantages of each technique, including their accuracy, cost-effectiveness, and ease of deployment. In the end, the paper predicts a few future research directions and potential opportunities for indoor localization. This paper will be of interest to researchers, practitioners, and industry professionals working on indoor localization and related fields.

APA, Harvard, Vancouver, ISO, and other styles

30

Morar, Anca, Alin Moldoveanu, Irina Mocanu, Florica Moldoveanu, Ion Emilian Radoi, Victor Asavei, Alexandru Gradinaru, and Alex Butean. "A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision." Sensors 20, no. 9 (May 6, 2020): 2641. http://dx.doi.org/10.3390/s20092641.

Full text

Abstract:

Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).

APA, Harvard, Vancouver, ISO, and other styles

31

Zhang, Li, Min Zhang, Jingao Xu, and Yi Xu. "Cluster-Based JRPCA Algorithm for Wi-Fi Fingerprint Localization." Electronics 12, no. 1 (December 29, 2022): 153. http://dx.doi.org/10.3390/electronics12010153.

Full text

Abstract:

Indoor localization services are emerging as an important application of the Internet of Things, which drives the development of related technologies in indoor scenarios. In recent years, various localization algorithms for different indoor scenarios have been proposed. The indoor localization algorithm based on fingerprints has attracted much attention due to its good performance without extra hardware devices. However, the occurrence of fingerprint mismatching caused by the complexity and variability of indoor scenarios is unneglectable, which degrades localization accuracy. In this article, by combining weighted kernel norm and L2,1-norm, a joint-norm robust principal component analysis (JRPCA in brief) assisted indoor localization algorithm is proposed, which can improve the localization accuracy through aggregating the reference points (RPs) and conducting robust feature extraction based on clustering. More specifically, a one-way hierarchical clustering termination method is proposed to obtain reasonable RP clusters adaptively according to the preset RPs. A two-phase fingerprint matching algorithm of JRPCA based on clustering is proposed to further increase the difference between similar RPs, thus facilitating rapid identification and reinforcing localization accuracy. To validate the proposed algorithm, extensive experiments are conducted in real indoor scenarios. The experimental results confirm that the proposed cluster-based JRPCA algorithm outperforms other existing algorithms in terms of robustness and accuracy.

APA, Harvard, Vancouver, ISO, and other styles

32

Sandamini, Chamali, Madduma Wellalage Pasan Maduranga, Valmik Tilwari, Jamaiah Yahaya, Faizan Qamar, Quang Ngoc Nguyen, and Siti Rohana Ahmad Ibrahim. "A Review of Indoor Positioning Systems for UAV Localization with Machine Learning Algorithms." Electronics 12, no. 7 (March 24, 2023): 1533. http://dx.doi.org/10.3390/electronics12071533.

Full text

Abstract:

The potential of indoor unmanned aerial vehicle (UAV) localization is paramount for diversified applications within large industrial sites, such as hangars, malls, warehouses, production lines, etc. In such real-time applications, autonomous UAV location is required constantly. This paper comprehensively reviews radio signal-based wireless technologies, machine learning (ML) algorithms and ranging techniques that are used for UAV indoor positioning systems. UAV indoor localization typically relies on vision-based techniques coupled with inertial sensing in indoor Global Positioning System (GPS)-denied situations, such as visual odometry or simultaneous localization and mapping employing 2D/3D cameras or laser rangefinders. This work critically reviews the research and systems related to mini-UAV localization in indoor environments. It also provides a guide and technical comparison perspective of different technologies, presenting their main advantages and disadvantages. Finally, it discusses various open issues and highlights future directions for UAV indoor localization.

APA, Harvard, Vancouver, ISO, and other styles

33

Yan, Suqing, Chunping Wu, Honggao Deng, Xiaonan Luo, Yuanfa Ji, and Jianming Xiao. "A Low-Cost and Efficient Indoor Fusion Localization Method." Sensors 22, no. 15 (July 23, 2022): 5505. http://dx.doi.org/10.3390/s22155505.

Full text

Abstract:

Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.

APA, Harvard, Vancouver, ISO, and other styles

34

Tiku, Saideep, Prathmesh Kale, and Sudeep Pasricha. "QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices." ACM Transactions on Cyber-Physical Systems 5, no. 4 (October 31, 2021): 1–30. http://dx.doi.org/10.1145/3461342.

Full text

Abstract:

Indoor localization services are a crucial aspect for the realization of smart cyber-physical systems within cities of the future. Such services are poised to reinvent the process of navigation and tracking of people and assets in a variety of indoor and subterranean environments. The growing ownership of computationally capable smartphones has laid the foundations of portable fingerprinting-based indoor localization through deep learning. However, as the demand for accurate localization increases, the computational complexity of the associated deep learning models increases as well. We present an approach for reducing the computational requirements of a deep learning-based indoor localization framework while maintaining localization accuracy targets. Our proposed methodology is deployed and validated across multiple smartphones and is shown to deliver up to 42% reduction in prediction latency and 45% reduction in prediction energy as compared to the best-known baseline deep learning-based indoor localization model.

APA, Harvard, Vancouver, ISO, and other styles

35

Chougule, Shreyanka B., and Sayed Abdulhayan. "INDOOR LOCALIZATION SOLUTION FOR GPS." International Journal of Research -GRANTHAALAYAH 5, no. 4RACEEE (April 30, 2017): 124–29. http://dx.doi.org/10.29121/granthaalayah.v5.i4raceee.2017.3334.

Full text

Abstract:

GPS technology is used for positioning application and it is highly reliable and accurate when used outdoor. Due to multipath propagation, signal attenuation and blockage its performance is limited in indoor and dense urban environment. As a solution, technologies like Apple’s iBeacon, Radio-frequency identification (RFID), Ultrasonic and Wireless Fidelity (Wi-Fi) access points are used to improve performance in Indoor environment. We are having a look at all these technologies which are meant for GPS Indoor performance improvement in this review paper.

APA, Harvard, Vancouver, ISO, and other styles

36

Che, Rongjie, and Honglong Chen. "Channel State Information Based Indoor Fingerprinting Localization." Sensors 23, no. 13 (June 22, 2023): 5830. http://dx.doi.org/10.3390/s23135830.

Full text

Abstract:

Indoor localization is one of the key techniques for location-based services (LBSs), which play a significant role in applications in confined spaces, such as tunnels and mines. To achieve indoor localization in confined spaces, the channel state information (CSI) of WiFi can be selected as a feature to distinguish locations due to its fine-grained characteristics compared with the received signal strength (RSS). In this paper, two indoor localization approaches based on CSI fingerprinting were designed: amplitude-of-CSI-based indoor fingerprinting localization (AmpFi) and full-dimensional CSI-based indoor fingerprinting localization (FuFi). AmpFi adopts the amplitude of the CSI as the localization fingerprint in the offline phase, and in the online phase, the improved weighted K-nearest neighbor (IWKNN) is proposed to estimate the unknown locations. Based on AmpFi, FuFi is proposed, which considers all of the subcarriers in the MIMO system as the independent features and adopts the normalized amplitudes of the full-dimensional subcarriers as the fingerprint. AmpFi and FuFi were implemented on a commercial network interface card (NIC), where FuFi outperformed several other typical fingerprinting-based indoor localization approaches.

APA, Harvard, Vancouver, ISO, and other styles

37

Liang, Xiaohu, Shuguo Pan, Baoguo Yu, Shuang Li, and Shitong Du. "A Pseudo-Satellite Fingerprint Localization Method Based on Discriminative Deep Belief Networks." Remote Sensing 16, no. 8 (April 18, 2024): 1430. http://dx.doi.org/10.3390/rs16081430.

Full text

Abstract:

Pseudo-satellite technology has excellent compatibility with the BDS satellite navigation system in terms of signal systems. It can serve as a stable and reliable positioning signal source in signal-blocking environments. User terminals can achieve continuous high-precision positioning both indoors and outdoors without any modification to the navigation module. As a result, pseudo-satellite indoor positioning has gradually emerged as a research hotspot in the field. However, due to the complex and variable indoor radio propagation environment, signal propagation is interfered with by noise, multipath, non-line-of-sight (NLOS) propagation, etc. The geometric relation-based localization algorithm cannot be applied in indoor non-line-of-sight environments. Therefore, this paper proposes a pseudo-satellite fingerprint localization method based on the discriminative deep belief networks (DDBNs). The method acquires the model parameters of pseudo-satellite multi-carrier noise density signal strength in non-line-of-sight indoor spaces through a greedy unsupervised learning method and gradient descent-supervised learning method. It establishes a mapping relationship between the implied features of the pseudo-satellite multi-carrier noise density signal strength and indoor location, enabling pseudo-satellite fingerprint matching localization in indoor non-line-of-sight environments. In this paper, the performance of the positioning algorithm is verified in dynamic and static scenarios through numerous experiments in a laboratory environment. Compared to the commonly used localization algorithms based on fingerprint library matching, the results demonstrate that, in indoor non-line-of-sight test conditions, the system’s 2D static positioning has a maximum error of less than 0.24 m, an RMSE better than 0.12 m, and a 2σ (95.4%) positioning error better than 0.19 m. For 2D dynamic positioning, the maximum error is less than 0.36 m, the average error is 0.23 m, and the 2σ positioning error is better than 0.26 m. These results effectively tackle the challenge of pseudo-satellite indoor positioning in non-line-of-sight environments.

APA, Harvard, Vancouver, ISO, and other styles

38

Yin, Jun, and Fengchun Yin. "Indoor Localization Based on Bluetooth." Artificial Intelligence Research 7, no. 2 (January 21, 2019): 87. http://dx.doi.org/10.5430/air.v7n2p87.

Full text

Abstract:

Global positioning system (GPS) has been widely used in positioning, vehicle navigation and other environments. However, in indoor environments, it can not achieve accurate positioning because of the weak signal through the wall. In other words, more appropriate techniques are needed in indoor scenes. Bluetooth technology has attracted more and more attention due to its advantages of low power consumption, wide coverage and fast transmission speed. Bluetooth-based indoor positioning refers to the indoor positioning technology that uses mobile terminal to receive Bluetooth signals from multiple Bluetooth devices, and calculates the location information of mobile terminal through the received information, so as to achieve high-precision positioning.In this paper, an effective optimal location algorithm is proposed. Firstly, the outlier detection algorithm is improved to remove the interference of abnormal data on the positioning accuracy; then, different filtering algorithms are used to process the received fingerprint information to ensure the accuracy of the fingerprint database establishment stage, and reduce the unnecessary construction time; finally, the average position is calculated by the average fingerprint data and judged the user's area.

APA, Harvard, Vancouver, ISO, and other styles

39

Gnaś, Dominik, and Przemysław Adamkiewicz. "INDOOR LOCALIZATION SYSTEM USING UWB." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 12, no. 1 (March 31, 2022): 15–19. http://dx.doi.org/10.35784/iapgos.2895.

Full text

Abstract:

This paper discusses two ways of measuring the distance between the transmitter and the receiver using UWB technology, then identifies their advantages and disadvantages. The method of calculating the position is presented together with the method of predicting errors based on room geometry. The hardware configuration of the transmitter and receiver systems included in a location system based on UWB technology is explained. Bluetooth technology is discussed, which is used to connect the location system with the environmental monitoring system.

APA, Harvard, Vancouver, ISO, and other styles

40

ARIF, M., S. WYNE, and A. JUNAID NAWAZ. "Indoor Localization using Voronoi Tessellation." Advances in Electrical and Computer Engineering 18, no. 4 (2018): 85–90. http://dx.doi.org/10.4316/aece.2018.04010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Ibrahim, Magdy, and Osama Moselhi. "Enhanced Localization for Indoor Construction." Procedia Engineering 123 (2015): 241–49. http://dx.doi.org/10.1016/j.proeng.2015.10.085.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Haque, Israat Tanzeena, and Chadi Assi. "Profiling-Based Indoor Localization Schemes." IEEE Systems Journal 9, no. 1 (March 2015): 76–85. http://dx.doi.org/10.1109/jsyst.2013.2281257.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Yin Chen, D. Lymberopoulos, Jie Liu, and B. Priyantha. "Indoor Localization Using FM Signals." IEEE Transactions on Mobile Computing 12, no. 8 (August 2013): 1502–17. http://dx.doi.org/10.1109/tmc.2013.58.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Gu, Fuqiang, Shahrokh Valaee, Kourosh Khoshelham, Jianga Shang, and Rui Zhang. "Landmark Graph-Based Indoor Localization." IEEE Internet of Things Journal 7, no. 9 (September 2020): 8343–55. http://dx.doi.org/10.1109/jiot.2020.2989501.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Kim, Tae-Kook. "IoT-based Indoor Localization Scheme." Journal of The Korea Internet of Things Society 2, no. 4 (December 30, 2016): 35–39. http://dx.doi.org/10.20465/kiots.2016.2.4.035.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Kong, Liang, Gavin Bauer, and John Hale. "Robust wireless signal indoor localization." Concurrency and Computation: Practice and Experience 27, no. 11 (June 5, 2015): 2839–50. http://dx.doi.org/10.1002/cpe.3443.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

AL-Khaleefa, Ahmed Salih, Mohd Riduan Ahmad, Azmi Awang Md Isa, Mona Riza Mohd Esa, Yazan Aljeroudi, Mohammed Ahmed Jubair, and Reza Firsandaya Malik. "Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization." Sensors 19, no. 10 (May 25, 2019): 2397. http://dx.doi.org/10.3390/s19102397.

Full text

Abstract:

Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc.

APA, Harvard, Vancouver, ISO, and other styles

48

Liu, J., C. Jiang, and Z. Shi. "THE DESIGN AND IMPLEMENTATION OF INDOOR LOCALIZATION SYSTEM USING MAGNETIC FIELD BASED ON SMARTPHONE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 379–83. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-379-2017.

Full text

Abstract:

Sufficient signal nodes are mostly required to implement indoor localization in mainstream research. Magnetic field take advantage of high precision, stable and reliability, and the reception of magnetic field signals is reliable and uncomplicated, it could be realized by geomagnetic sensor on smartphone, without external device. After the study of indoor positioning technologies, choose the geomagnetic field data as fingerprints to design an indoor localization system based on smartphone. A localization algorithm that appropriate geomagnetic matching is designed, and present filtering algorithm and algorithm for coordinate conversion. With the implement of plot geomagnetic fingerprints, the indoor positioning of smartphone without depending on external devices can be achieved. Finally, an indoor positioning system which is based on Android platform is successfully designed, through the experiments, proved the capability and effectiveness of indoor localization algorithm.

APA, Harvard, Vancouver, ISO, and other styles

49

Yang, Y., C. Toth, and D. Brzezinska. "A 3D MAP AIDED DEEP LEARNING BASED INDOOR LOCALIZATION SYSTEM FOR SMART DEVICES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 391–97. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-391-2020.

Full text

Abstract:

Abstract. Indoor positioning technologies represent a fast developing field of research due to the rapidly increasing need for indoor location-based services (ILBS); in particular, for applications using personal smart devices. Recently, progress in indoor mapping, including 3D modeling and semantic labeling started to offer benefits to indoor positioning algorithms; mainly, in terms of accuracy. This work presents a method for efficient and robust indoor localization, allowing to support applications in large-scale environments. To achieve high performance, the proposed concept integrates two main indoor localization techniques: Wi-Fi fingerprinting and deep learning-based visual localization using 3D map. The robustness and efficiency of technique is demonstrated with real-world experiences.

APA, Harvard, Vancouver, ISO, and other styles

50

Chen, Shi. "Indoor Localization Based on Fingerprint Clustering." Network and Communication Technologies 5, no. 2 (December 31, 2020): 40. http://dx.doi.org/10.5539/nct.v5n2p40.

Full text

Abstract:

With the rapid development of the huge promotion of the Internet and artificial intelligence, the demand for location-based services in indoor environments has grown rapidly. At present, for the localization of the indoor environment, researchers from all walks of life have proposed many indoor localization solutions based on different technologies. Fingerprint localization technology, as a commonly used indoor localization technology, has led to continuous research and improvement due to its low accuracy and complex calculations. An indoor localization system based on fingerprint clustering is proposed by this paper. The system includes offline phase and online phase. We collect the RSS signal in the offline phase. We preprocess it with the Gaussian model to build a fingerprint database, and then we use the K-Means++ algorithm to cluster the fingerprints and group the fingerprints with similar signal strengths into a clustering subset. In the online phase, we classify the measured received signal strength (RSS), and then use the weighted K-Nearest neighbor (WKNN) algorithm to calculate the localization error. The experimental results show that we can reduce the localization error and effectively reduce the computational cost of the localization algorithm in the online phase, and effectively improve the efficiency of real-time localization in the online phase.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Indoor localization'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles