Journal articles: 'Wi-Fi Indoor positioning system'

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Poulose, Alwin, and Dong Seog Han. "Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications." Electronics 10, no. 1 (December 22, 2020): 2. http://dx.doi.org/10.3390/electronics10010002.

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Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

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Zhang, Wei, Xianghong Hua, Kegen Yu, Weining Qiu, Shoujian Zhang, and Xiaoxing He. "A novel WiFi indoor positioning strategy based on weighted squared Euclidean distance and local principal gradient direction." Sensor Review 39, no. 1 (January 21, 2019): 99–106. http://dx.doi.org/10.1108/sr-06-2017-0109.

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Purpose This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the received signal strength-based Wi-Fi indoor positioning, a low-cost indoor positioning approach, has attracted a significant attention from both academia and industry. Design/methodology/approach The local principal gradient direction is introduced and used to define the weighting function and an average algorithm based on k-means algorithm is used to estimate the local principal gradient direction of each access point. Then, correlation distance is used in the new method to find the k nearest calibration points. The weighted squared Euclidean distance between the nearest calibration point and target point is calculated and used to estimate the position of target point. Findings Experiments are conducted and the results indicate that the proposed Wi-Fi indoor positioning approach considerably outperforms the weighted k nearest neighbor method. The new method also outperforms support vector regression and extreme learning machine algorithms in the absence of sufficient fingerprints. Research limitations/implications Weighted k nearest neighbor approach, support vector regression algorithm and extreme learning machine algorithm are the three classic strategies for location determination using Wi-Fi fingerprinting. However, weighted k nearest neighbor suffers from dramatic performance degradation in the presence of multipath signal attenuation and environmental changes. More fingerprints are required for support vector regression algorithm to ensure the desirable performance; and labeling Wi-Fi fingerprints is labor-intensive. The performance of extreme learning machine algorithm may not be stable. Practical implications The new weighted squared Euclidean distance-based Wi-Fi indoor positioning strategy can improve the performance of Wi-Fi indoor positioning system. Social implications The received signal strength-based effective Wi-Fi indoor positioning system can substitute for global positioning system that does not work indoors. This effective and low-cost positioning approach would be promising for many indoor-based location services. Originality/value A novel Wi-Fi indoor positioning strategy based on the weighted squared Euclidean distance is proposed in this paper to improve the performance of the Wi-Fi indoor positioning, and the local principal gradient direction is introduced and used to define the weighting function.

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Muroň, Mikuláš, and David Procházka. "Wi‑Fi Indoor Localisation: A Deeper Insight Into Patterns in the Fingerprint Map Data." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 66, no. 6 (2018): 1565–71. http://dx.doi.org/10.11118/actaun201866061565.

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Localisation via Wi‑Fi networks is one of the possible techniques which can be used for positioning inside buildings or in other places without the GPS signal. The accurate indoor positioning system can help users with localisation or navigation within unfamiliar places. Almost all buildings are covered with the Wi‑Fi signal. Using the currently existing infrastructure will minimise cost for construction other types of indoor positioning systems. Among other reasons, usage of Wi‑Fi for positioning is also convenient because almost every mobile device has a Wi‑Fi capability and therefore the system can be easily used by everyone. However, an important factor is the precision of such a solution. The article is focused on the evaluation of Wi‑Fi localisation precision within the university grounds.

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Cui, Wei, Qingde Liu, Linhan Zhang, Haixia Wang, Xiao Lu, and Junliang Li. "A robust mobile robot indoor positioning system based on Wi-Fi." International Journal of Advanced Robotic Systems 17, no. 1 (January 1, 2020): 172988141989666. http://dx.doi.org/10.1177/1729881419896660.

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Recently, most of the existing mobile robot indoor positioning systems (IPSs) use infrared sensors, cameras, and other extra infrastructures. They usually suffer from high cost and special hardware implementation. In order to address the above problems, this article proposes a Wi-Fi-based indoor mobile robot positioning system and designs and develops a robot positioning platform based on the commercial Wi-Fi devices, such as Wi-Fi routers. Furthermore, a robust principal component analysis-based extreme learning machine algorithm is proposed to address the issue of noisy measurements in IPSs. Real-world robot indoor positioning experiments are extensively carried out and the results verify the effectiveness and superiority of the proposed system.

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Lukito, Yuan. "Multi Layer Perceptron Model for Indoor Positioning System Based on Wi-Fi." Jurnal Teknologi dan Sistem Komputer 5, no. 3 (July 31, 2017): 123–28. http://dx.doi.org/10.14710/jtsiskom.5.3.2017.123-128.

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Indoor positioning system issue is an open problem that still needs some improvements. This research explores the utilization of multilayer perceptron in determining someone’s position inside a building or a room, which generally known as Indoor Positioning System. The research was conducted in some steps: dataset normalization, multilayer perceptron implementation, training process of multilayer perceptron, evaluation, and analysis. The training process has been conducted many times to find the best parameters that produce the best accuracy rate. The experiment produces 79,16% as the highest accuracy rate. Compared to previous research, this result is comparably lower and needs some parameters tweaking or changing the neural networks architectures.

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Haider, Amir, Yiqiao Wei, Shuzhi Liu, and Seung-Hoon Hwang. "Pre- and Post-Processing Algorithms with Deep Learning Classifier for Wi-Fi Fingerprint-Based Indoor Positioning." Electronics 8, no. 2 (February 8, 2019): 195. http://dx.doi.org/10.3390/electronics8020195.

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To accommodate the rapidly increasing demand for connected infrastructure, automation for industrial sites and building smart cities, the development of Internet of Things (IoT)-based solutions is considered one of the major trends in modern day industrial revolution. In particular, providing high precision indoor positioning services for such applications is a key challenge. Wi-Fi fingerprint-based indoor positioning systems have been adapted as promising candidates for such applications. The performance of such indoor positioning systems degrade drastically due to several impairments like noisy datasets, high variation in Wi-Fi signals over time, fading of Wi-Fi signals due to multipath propagation caused by hurdles, people walking in the area under consideration and the addition/removal of Wi-Fi access points (APs). In this paper, we propose data pre- and post-processing algorithms with deep learning classifiers for Wi-Fi fingerprint-based indoor positioning, in order to provide immunity against limitations in the database and the indoor environment. In addition, we investigate the performance of the proposed system through simulation as well as extensive experiments. The results demonstrate that the pre-processing algorithm can efficiently fill in the missing Wi-Fi received signal strength fingerprints in the database, resulting in a success rate of 88.96% in simulation and 86.61% in a real-time experiment. The post-processing algorithm can improve the results from 9.05–10.94% for the conducted experiments, providing the highest success rate of 95.94% with a precision of 4 m for Wi-Fi fingerprint-based indoor positioning.

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Ali, Muhammad, Soojung Hur, and Yongwan Park. "Wi-Fi-Based Effortless Indoor Positioning System Using IoT Sensors." Sensors 19, no. 7 (March 27, 2019): 1496. http://dx.doi.org/10.3390/s19071496.

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Wi-Fi positioning based on fingerprinting has been considered as the most widely used technology in the field of indoor positioning. The fingerprinting database has been used as an essential part of the Wi-Fi positioning system. However, the offline phase of the calibration involves a laborious task of site analysis which involves costs and a waste of time. We offer an indoor positioning system based on the automatic generation of radio maps of the indoor environment. The proposed system does not require any effort and uses Wi-Fi compatible Internet-of-Things (IoT) sensors. Propagation loss parameters are automatically estimated from the online feedback of deployed sensors and the radio maps are updated periodically without any physical intervention. The proposed system leverages the raster maps of an environment with the wall information only, against computationally extensive techniques based on vector maps that require precise information on the length and angles of each wall. Experimental results show that the proposed system has achieved an average accuracy of 2 m, which is comparable to the survey-based Wi-Fi fingerprinting technique.

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Yu, C., and N. El-Sheimy. "INDOOR MAP AIDED INS/WI-FI INTEGRATED LBS ON SMARTPHONE PLATFORMS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4 (September 14, 2017): 425–29. http://dx.doi.org/10.5194/isprs-annals-iv-2-w4-425-2017.

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In this research, an indoor map aided INS/Wi-Fi integrated location based services (LBS) applications is proposed and implemented on smartphone platforms. Indoor map information together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value from Wi-Fi are collected to obtain an accurate, continuous, and low-cost position solution. The main challenge of this research is to make effective use of various measurements that complement each other without increasing the computational burden of the system. The integrated system in this paper includes three modules: INS, Wi-Fi (if signal available) and indoor maps. A cascade structure Particle/Kalman filter framework is applied to combine the different modules. Firstly, INS position and Wi-Fi fingerprint position integrated through Kalman filter for estimating positioning information. Then, indoor map information is applied to correct the error of INS/Wi-Fi estimated position through particle filter. Indoor tests show that the proposed method can effectively reduce the accumulation positioning errors of stand-alone INS systems, and provide stable, continuous and reliable indoor location service.

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Leca, Cristian-Liviu, Ioan Nicolaescu, and Petrica Ciotirnae. "Crowdsensing Influences and Error Sources in Urban Outdoor Wi-Fi Fingerprinting Positioning." Sensors 20, no. 2 (January 12, 2020): 427. http://dx.doi.org/10.3390/s20020427.

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Wi-Fi fingerprinting positioning systems have been deployed for a long time in location-based services for indoor environments. Combining mobile crowdsensing and Wi-Fi fingerprinting systems could reduce the high cost of collecting the necessary data, enabling the deployment of the resulting system for outdoor positioning in areas with dense Wi-Fi coverage. In this paper, we present the results attained in the design and evaluation of an urban fingerprinting positioning system based on crowdsensed Wi-Fi measurements. We first assess the quality of the collected measurements, highlighting the influence of received signal strength on data collection. We then evaluate the proposed system by comparing the influence of the crowdsensed fingerprints on the overall positioning accuracy for different scenarios. This evaluation helps gain valuable insight into the design and deployment of urban Wi-Fi positioning systems while also allowing the proposed system to match GPS-like accuracy in similar conditions.

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Shin, Geon-Sik, and Yong-Hyeon Shin. "Wi-Fi Based Indoor Positioning System Using Hybrid Algorithm." Journal of Advanced Navigation Technology 19, no. 6 (December 30, 2015): 564–73. http://dx.doi.org/10.12673/jant.2015.19.6.564.

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Lassabe, F., P. Canalda, P. Chatonnay, and F. Spies. "Indoor Wi-Fi positioning: techniques and systems." annals of telecommunications - annales des télécommunications 64, no. 9-10 (July 22, 2009): 651–64. http://dx.doi.org/10.1007/s12243-009-0122-1.

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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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Górak, Rafał, and Marcin Luckner. "Automatic Detection of Missing Access Points in Indoor Positioning System †." Sensors 18, no. 11 (October 23, 2018): 3595. http://dx.doi.org/10.3390/s18113595.

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The paper presents a Wi-Fi-based indoor localisation system. It consists of two main parts, the localisation model and an Access Points (APs) detection module. The system uses a received signal strength (RSS) gathered by multiple mobile terminals to detect which AP should be included in the localisation model and whether the model needs to be updated (rebuilt). The rebuilding of the localisation model prevents the localisation system from a significant loss of accuracy. The proposed automatic detection of missing APs has a universal character and it can be applied to any Wi-Fi localisation model which was created using the fingerprinting method. The paper considers the localisation model based on the Random Forest algorithm. The system was tested on data collected inside a multi-floor academic building. The proposed implementation reduced the mean horizontal error by 5.5 m and the classification error for the floor’s prediction by 0.26 in case of a serious malfunction of a Wi-Fi infrastructure. Several simulations were performed, taking into account different occupancy scenarios as well as different numbers of missing APs. The simulations proved that the system correctly detects missing and present APs in the Wi-Fi infrastructure.

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Selamat, M. H., and An Narzullaev. "WI-FI SIGNAL STRENGTH VS. MAGNETIC FIELDS FOR INDOOR POSITIONING SYSTEMS." Eurasian Journal of Mathematical and Computer Applications 2, no. 1 (2014): 122–33. http://dx.doi.org/10.32523/2306-3172-2014-2-2-122-133.

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Yu, Hu, and Wang. "A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning." ISPRS International Journal of Geo-Information 8, no. 11 (October 23, 2019): 468. http://dx.doi.org/10.3390/ijgi8110468.

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The uncertainty of indoor Wi-Fi positioning is susceptible to many factors, such as sensor distribution, the internal environment (e.g., of a shopping mall), differences between receivers, and the flow of people. In this paper, an indoor pedestrian trajectory pattern mining approach for the assessment of the error and accuracy of indoor Wi-Fi positioning is proposed. First, the stay points of the customer were extracted from the pedestrian trajectories based on the spatiotemporal staying patterns of the customers in a shopping mall. Second, the drift points were distinguished from the stay points through analysis of noncustomer behavior patterns. Finally, the drift points were presented to calculate the errors in the pedestrian trajectories for the accuracy assessment of the indoor Wi-Fi positioning system. A one-month indoor pedestrian trajectories dataset from the Xinxiang Baolong shopping mall in Henan Province, China, was used for the assessment of the error and accuracy values with the proposed approach. The experimental results were verified by incorporating the distribution of the AP sensors. The proposed approach using big data pattern mining can explore the error distribution of indoor positioning systems, which can provide strong support for improving indoor positioning accuracy in the future.

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Jamaluddin, J., Agung Tjahjo Nugroho, and Wenny Maulina. "Rancang Bangun Indoor Positioning System berbasis Wireless Smartphone menggunakan Teknik Global Positioning System dengan Metode Absolut." BERKALA SAINSTEK 7, no. 1 (March 13, 2019): 13. http://dx.doi.org/10.19184/bst.v7i1.9914.

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Indoor Positioning System (IPS) merupakan teknologi informasi untuk menentukan posisi objek di dalam ruangan berbasis wireless smartphone. Perangkat yang digunakan dalam penelitian ini adalah empat unit smartphone, satu smartphone sebagai transmitter, dan tiga smartphone lainnya sebagai receiver. Tujuan penelitian ini adalah mendapatkan model dan tingkat akurasi dari IPS berbasis wireless smartphone menggunakan teknik Global Positioning System (GPS) dengan metode absolut. Penelitian ini dilakukan dengan membuat dua model IPS dan melakukan pengukuran intensitas sinyal Wi-Fi berdasarkan masing-masing model IPS yang telah dibuat untuk mendapatkan persamaan linier antara jarak dan intensitas sinyal Wi-Fi. Persamaan linier yang didapatkan dari model IPS digunakan untuk menentukan jarak antara receiver dan transmitter berdasarkan intensitas sinyal Wi-Fi yang terukur pada saat pengujian model, kemudian informasi jarak tersebut digunakan untuk menentukan posisi objek (transmitter). Hasil penelitian menunjukkan bahwa Model 1 IPS berbasis wireless smartphone mampu mengestimasi posisi dengan rata-rata tingkat kesalahan mencapai 4,46 m dan tingkat akurasinya mencapai 76,51%. Model 2 IPS mampu mengestimasi posisi dengan rata-rata tingkat kesalahan 9,68 m dengan tingkat akurasinya mencapai 49,03%. Berdsarakan hasil tersebut, dapat disimpulkan bahwa model 1 IPS memiliki tingkat akurasi yang lebih baik untuk mengestimasi posisi objek daripada model 2 IPS. Kata Kunci: Global positioning system (GPS), indoor positioning system (IPS), wireless smartphone.

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Yu, Yue, Ruizhi Chen, Zuoya Liu, Guangyi Guo, Feng Ye, and Liang Chen. "Wi-Fi Fine Time Measurement: Data Analysis and Processing for Indoor Localisation." Journal of Navigation 73, no. 5 (May 4, 2020): 1106–28. http://dx.doi.org/10.1017/s0373463320000193.

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Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Wi-Fi based indoor localisation has become attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which can be used for Wi-Fi ranging between intelligent terminal and Wi-Fi access point. This paper introduces a framework of Wi-Fi FTM data acquisition and processing that can be used for indoor localisation. We analyse the main factors that affect the accuracy of Wi-Fi ranging and propose a calibration, filtering and modelling algorithm that can effectively reduce the ranging error caused by clock deviation, non-line-of-sight (NLOS) and multipath propagation. Experimental results show that the proposed calibration and filtering method is able to achieve metre-level ranging accuracy in case of line-of-sight by using large bandwidth. Estimation results also show that the proposed Wi-Fi ranging model provides an accurate ranging performance in NLOS and multipath contained indoor environment; the final positioning error is less than 2·2 m with a stable output frequency of 3 Hz.

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Adege, Abebe Belay, Hsin-Piao Lin, Getaneh Berie Tarekegn, Yirga Yayeh Munaye, and Lei Yen. "An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms." Journal of Sensors 2018 (December 16, 2018): 1–12. http://dx.doi.org/10.1155/2018/1253752.

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Indoor and outdoor positioning lets to offer universal location services in industry and academia. Wi-Fi and Global Positioning System (GPS) are the promising technologies for indoor and outdoor positioning, respectively. However, Wi-Fi-based positioning is less accurate due to the vigorous changes of environments and shadowing effects. GPS-based positioning is also characterized by much cost, highly susceptible to the physical layouts of equipment, power-hungry, and sensitive to occlusion. In this paper, we propose a hybrid of support vector machine (SVM) and deep neural network (DNN) to develop scalable and accurate positioning in Wi-Fi-based indoor and outdoor environments. In the positioning processes, we primarily construct real datasets from indoor and outdoor Wi-Fi-based environments. Secondly, we apply linear discriminate analysis (LDA) to construct a projected vector that uses to reduce features without affecting information contents. Thirdly, we construct a model for positioning through the integration of SVM and DNN. Fourthly, we use online datasets from unknown locations and check the missed radio signal strength (RSS) values using the feed-forward neural network (FFNN) algorithm to fill the missed values. Fifthly, we project the online data through an LDA-based projected vector. Finally, we test the positioning accuracies and scalabilities of a model created from a hybrid of SVM and DNN. The whole processes are implemented using Python 3.6 programming language in the TensorFlow framework. The proposed method provides accurate and scalable positioning services in different scenarios. The results also show that our proposed approach can provide scalable positioning, and 100% of the estimation accuracies are with errors less than 1 m and 1.9 m for indoor and outdoor positioning, respectively.

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Salamah, Ahmed H., Mohamed Tamazin, Maha A. Sharkas, Mohamed Khedr, and Mohamed Mahmoud. "Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization." Sensors 19, no. 7 (April 8, 2019): 1678. http://dx.doi.org/10.3390/s19071678.

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The smartphone market is rapidly spreading, coupled with several services and applications. Some of these services require the knowledge of the exact location of their handsets. The Global Positioning System (GPS) suffers from accuracy deterioration and outages in indoor environments. The Wi-Fi Fingerprinting approach has been widely used in indoor positioning systems. In this paper, Principal Component Analysis (PCA) is utilized to improve the performance and to reduce the computation complexity of the Wi-Fi indoor localization systems based on a machine learning approach. The experimental setup and performance of the proposed method were tested in real indoor environments at a large-scale environment of 960 m2 to analyze the performance of different machine learning approaches. The results show that the performance of the proposed method outperforms conventional indoor localization techniques based on machine learning techniques.

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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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Chai, Meiling, Changgeng Li, and Hui Huang. "A New Indoor Positioning Algorithm of Cellular and Wi-Fi Networks." Journal of Navigation 73, no. 3 (December 11, 2019): 509–29. http://dx.doi.org/10.1017/s0373463319000742.

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Fluctuation of the received signal strength (RSS) is the key performance-limiting factor for Wi-Fi indoor positioning schemes. In this study, the Manhattan distance was used in the weighted K-nearest neighbour (WKNN) algorithm to improve positioning accuracy. Reference point (RP) intervals were optimised to reduce the complexity of the system. Specifically, two new positioning schemes are proposed in this paper. Scheme 1 uses the cellular network to refine the fingerprint database, while Scheme 2 uses the cellular network positioning to locate the node a priori, then uses the Wi-Fi network to further improve accuracy. The experimental results showed that the average positioning error of Scheme 1 was 1·60 m, a reduction of 12% compared with the existing Wi-Fi fingerprinting schemes. In Scheme 2, when double cellular networks were used, RP usage was reduced by 64% and the calculating time was 0·24 s, a reduction of up to 69·5% compared with the Manhattan-WKNN algorithm. These proposed schemes are suitable for high accuracy and real-time positioning situations, respectively.

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Lee, Dong Myung, and Boney Labinghisa. "Indoor localization system based on virtual access points with filtering schemes." International Journal of Distributed Sensor Networks 15, no. 7 (July 2019): 155014771986613. http://dx.doi.org/10.1177/1550147719866135.

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In indoor positioning techniques, Wi-Fi is one of the most used technology because of its availability and cost-effectiveness. Access points are usually the main source of Wi-Fi signals in an indoor environment. If access points are optimized to cover the indoor area, this could improve Wi-Fi signal distribution. This article proposed an alternative to optimizing access point placement and distribution by introducing virtual access points that can be virtually placed in any part of the indoor environment without installation of actual access points. Virtual access points will be created heuristically by correlating received signal strength indicator of already existing access points and through linear regression. After introducing virtual access points in the indoor environment, next will be the addition of filters to improve signal fluctuation and reduce noise interference. Kalman filter has been previously used together with virtual access point and showed improvement by decreasing error distance of Wi-Fi fingerprinting results. This article also aims to include particle filter in the system to further improve localization and test its effectiveness when paired with Kalman filter. The performance testing of the algorithm in different indoor environments resulted in 3.18 and 3.59 m error distances. An improvement was added on the system by using relative distances instead of received signal strength indicator values in distance estimation and gave an error distance average of 1.85 m.

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Papliatseyeu, Andrei, Venet Osmani, and Oscar Mayora. "Indoor Positioning Using FM Radio." International Journal of Handheld Computing Research 1, no. 3 (July 2010): 19–31. http://dx.doi.org/10.4018/jhcr.2010070102.

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This paper presents an indoor positioning system based on FM radio. The system is built on commercially available short-range FM transmitters. This is the first experimental study of FM performance for indoor localisation. FM radio possesses a number of features, which make it distinct from other localisation technologies. Despite the low cost and off-the-shelf components, this FM positioning system reaches a high performance, comparable to other positioning technologies such as Wi-Fi. The authors’ experiments have yielded a median accuracy of 1.0 m and in 95% of cases the error is below 5 m.

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Ashraf, Hur, and Park. "Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area." Sensors 19, no. 19 (October 8, 2019): 4351. http://dx.doi.org/10.3390/s19194351.

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The applications of location-based services require precise location information of a user both indoors and outdoors. Global positioning system’s reduced accuracy for indoor environments necessitated the initiation of Indoor Positioning Systems (IPSs). However, the development of an IPS which can determine the user’s position with heterogeneous smartphones in the same fashion is a challenging problem. The performance of Wi-Fi fingerprinting-based IPSs is degraded by many factors including shadowing, absorption, and interference caused by obstacles, human mobility, and body loss. Moreover, the use of various smartphones and different orientations of the very same smartphone can limit its positioning accuracy as well. As Wi-Fi fingerprinting is based on Received Signal Strength (RSS) vector, it is prone to dynamic intrinsic limitations of radio propagation, including changes over time, and far away locations having similar RSS vector. This article presents a Wi-Fi fingerprinting approach that exploits Wi-Fi Access Points (APs) coverage area and does not utilize the RSS vector. Using the concepts of APs coverage area uniqueness and coverage area overlap, the proposed approach calculates the user’s current position with the help of APs’ intersection area. The experimental results demonstrate that the device dependency can be mitigated by making the fingerprinting database with the proposed approach. The experiments performed at a public place proves that positioning accuracy can also be increased because the proposed approach performs well in dynamic environments with human mobility. The impact of human body loss is studied as well.

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Yee Tan, Siok. "A Shopping Mall Indoor Navigation Application using Wi-Fi Positioning System." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4 (August 25, 2020): 4483–89. http://dx.doi.org/10.30534/ijatcse/2020/42942020.

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Hrad, Jaromir, Lukas Vojtech, Martin Cihlar, Pavel Stasa, Marek Neruda, Filip Benes, and Jiri Svub. "Indoor Positioning System Based on Fuzzy Logic and WLAN Infrastructure." Sensors 20, no. 16 (August 11, 2020): 4490. http://dx.doi.org/10.3390/s20164490.

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This paper deals with the issue of mobile device localization in the environment of buildings, which is suitable for use in healthcare or crisis management. The developed localization system is based on wireless Local Area Network (LAN) infrastructure (commonly referred to as Wi-Fi), evaluating signal strength from different access points, using the fingerprinting method for localization. The most serious problems consist in multipath signal propagation and the different sensitivities (calibration) of Wi-Fi adapters installed in different mobile devices. To solve these issues, an algorithm based on fuzzy logic is proposed to optimize the localization performance. The localization system consists of five elements, which are mobile applications for Android OS, a fuzzy derivation model, and a web surveillance environment for displaying the localization results. All of these elements use a database and shared storage on a virtualized server running Ubuntu. The developed system is implemented in Java for Android-based mobile devices and successfully tested. The average accuracy is satisfactory for determining the position of a client device on the level of rooms.

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Wang, Haixia, Junliang Li, Wei Cui, Xiao Lu, Zhiguo Zhang, Chunyang Sheng, and Qingde Liu. "Mobile Robot Indoor Positioning System Based on K-ELM." Journal of Sensors 2019 (February 14, 2019): 1–11. http://dx.doi.org/10.1155/2019/7547648.

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Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

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Torres-Sospedra, Joaquín, Raul Montoliu, Germán M. Mendoza-Silva, Oscar Belmonte, David Rambla, and Joaquín Huerta. "Providing Databases for Different Indoor Positioning Technologies: Pros and Cons of Magnetic Field and Wi-Fi Based Positioning." Mobile Information Systems 2016 (2016): 1–22. http://dx.doi.org/10.1155/2016/6092618.

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Localization is one of the main pillars for indoor services. However, it is still very difficult for the mobile sensing community to compare state-of-the-art indoor positioning systems due to the scarcity of publicly available databases. To make fair and meaningful comparisons between indoor positioning systems, they must be evaluated in the same situation, or in the same sets of situations. In this paper, two databases are introduced for studying the performance of magnetic field and Wi-Fi fingerprinting based positioning systems in the same environment (i.e., indoor area). The “magnetic” database contains more than 40,000 discrete captures (270 continuous samples), whereas the “Wi-Fi” one contains 1,140 ones. The environment and both databases are fully detailed in this paper. A set of experiments is also presented where two simple but effective baselines have been developed to test the suitability of the databases. Finally, the pros and cons of both types of positioning techniques are discussed in detail.

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Sansano-Sansano, Emilio, Óscar Belmonte-Fernández, Raúl Montoliu, Arturo Gascó-Compte, and Antonio Caballer-Miedes. "Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments." Mobile Information Systems 2020 (August 25, 2020): 1–16. http://dx.doi.org/10.1155/2020/5204158.

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A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal Strength Indication (RSSI) mapping, avoids the need to deploy a dedicated positioning infrastructure but comes with its own issues. Heterogeneity of devices and RSSI variability in space and time due to environment changing conditions pose a challenge to positioning systems based on this technique. The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smartphones or smartwatches, can increase the performance of Indoor Positioning Systems in AAL environments. Furthermore, this paper assesses a set of techniques to predict the future performance of the positioning system based on the training data, as well as complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies.

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Zhang, Xiaoxiang, Peiquan Jin, Lihua Yue, Na Wang, and Qianyuan Li. "Towards Mobile Information Systems for Indoor Space." Mobile Information Systems 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/9673048.

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With the rapid development of Internet of things (IOT) and indoor positioning technologies such as Wi-Fi and RFID, indoor mobile information systems have become a new research hotspot. Based on the unique features of indoor space and urgent needs on indoor mobile applications, in this paper we analyze some key issues in indoor mobile information systems, including positioning technologies in indoor environments, representation models for indoor spaces, query processing techniques for indoor moving objects, and index structures for indoor mobile applications. Then, we present an indoor mobile information management system named IndoorDB. Finally, we give some future research topics about indoor mobile information systems.

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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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Yang, Y., and C. Toth. "INDOOR POSITIONING FOR SMART DEVICES BASED ON SENSOR FUSION WITH PARTICLE FILTER: LOCALIZATION AND MAP UPDATING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June 30, 2021): 259–66. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2021-259-2021.

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Abstract. With every new generation of smart devices, new sensors are introduced, such as depth camera or UWB sensors. Combined with the rapidly growing number of smart mobile devices, indoor positioning systems (IPS) have seen increasing interest due to numerous indoor location-based services (ILBS) and mobile applications at large. Wi-Fi Received Signal Strength (RSS) based fingerprinting positioning (WF) techniques are popularly used in many IPS as the widespread deployment of IEEE 802.11 WLAN (Wi-Fi) networks, as this technique requires no line-of-sight to the access points (APs), and it is easy to extract Wi-Fi signal from 802.11 networks with smart devices. However, WF techniques have problems with fingerprint variance, i.e., fluctuation of the sensed signal, and efficient map updating due to the frequently changing environment. To address these problems, we propose a novel framework of IPS which uses particle filter to fuse WF and state-of-the-art CNN-based visual localization method to better adapt to changing indoor environment. The suggested system was tested with real-world crowdsourced data collected by multiple devices in an office hallway. The experimental results demonstrate that the system can achieve robust localization at a 0.3~1.5 m mean error (ME) accuracy, and map updating with a 79% correction rate.

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Na, Dong-Jun, and Kwon-Hue Choi. "Step Trajectory/Indoor Map Feature-based Smartphone Indoor Positioning System without Using Wi-Fi Signals." Journal of IEMEK 9, no. 6 (December 31, 2014): 323–34. http://dx.doi.org/10.14372/iemek.2014.9.6.323.

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López-Pastor, José-Antonio, Antonio-Jesús Ruiz-Ruiz, Antonio-Javier García-Sánchez, and José-Luis Gómez-Tornero. "An Automatized Contextual Marketing System Based on a Wi-Fi Indoor Positioning System." Sensors 21, no. 10 (May 17, 2021): 3495. http://dx.doi.org/10.3390/s21103495.

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A complete contextual marketing platform including an indoor positioning system (IPS) for smartphones is proposed and evaluated to later be deployed in large infrastructures, such as malls. To this end, we design and implement a novel methodology based on location-as-a-service (LAAS), comprising all the required phases of IPS generation: mall digital map creation, the tools/procedures for offline calibration fingerprint acquisition, the location algorithm, the smartphone app acquiring the fingerprint data, and a validation procedure. To select an appropriate fingerprint location algorithm, a comparison among K-nearest neighbors (KNN), support vector machine (SVM), and Freeloc is accomplished by employing a set of different smartphones in two malls and assessing different occupancy levels. We demonstrate that our solution can be quickly deployed at shop level accuracy in any new location, resulting in a robust and scalable proposal.

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Kareem, Amnah A., Wissam H. Ali, and Manal H. Jasim. "Design and Implementation of a Wireless System to Locate a User in Indoor Environments." Engineering and Technology Journal 38, no. 11A (November 25, 2020): 1640–51. http://dx.doi.org/10.30684/etj.v38i11a.1592.

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The technology of indoor positioning has pulled in the consideration of researchers the expanding capability of smartphones and the advancement of sensor innovation, alongside the increase the time people spend working inside the building or being indoors. Sensor innovation, which is one of the most generally utilized information hotspots for indoor setting, has a favorable position that sensors can receive information from a cell phone without introducing any additional device. The idea of the proposed system is to use the Wi-Fi access points, inside the building, together with a Smartphone Wi-Fi sensor which lets the building administrator locate those carrying smartphones, wherever they exist inside the building. The proposed system consists of two-stage the testing stage (or preparation phase) and, the second stage is the training stage (or positioning phase). The data is collected and selected for accurate readings; a router is used, which is the Mikrotik access point type from which we can read the RSS value. The RSS value represents the Wi-Fi signal strength of the target device. The proposed IPS detection system is independent and can work in unconstrained environments. The database used to measure the performance of the proposed IPS detection system is collected from 14 locations (different in size). The number of readings obtained from the collected database is 1199 readings consist of received signal strength value from five access points. The proposed IPS accuracy is 96.8595% and the mean error is about 1.2 meters are achieved when using, K-Nearest Neighbor (K-NN), used the...

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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.

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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.

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Chen, Chen, Yi Han, Yan Chen, and K. J. Ray Liu. "Indoor Global Positioning System with Centimeter Accuracy Using Wi-Fi [Applications Corner]." IEEE Signal Processing Magazine 33, no. 6 (November 2016): 128–34. http://dx.doi.org/10.1109/msp.2016.2600734.

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Kim, Wooseong, Sungwon Yang, Mario Gerla, and Eun-Kyu Lee. "Crowdsource Based Indoor Localization by Uncalibrated Heterogeneous Wi-Fi Devices." Mobile Information Systems 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/4916563.

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Many indoor localization techniques that rely on received signals from Wi-Fi access points have been explored in the last decade. Recently, crowdsourced Wi-Fi fingerprint attracts much attention, which leads to a self-organized localization system avoiding painful survey efforts. However, this participatory approach introduces new challenges with no previously proposed techniques such as heterogeneous devices, short measurement time, and multiple values for a single position. This paper proposes an efficient localization method combating the three major technical issues in the crowdsourcing based systems. We evaluate our indoor positioning method using 5 places with different radio environment and 8 different mobile phones. The experimental results show that the proposed approach provides consistent localization accuracy and outperforms existing localization algorithms.

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Fernandes, Letícia, Sara Santos, Marília Barandas, Duarte Folgado, Ricardo Leonardo, Ricardo Santos, André Carreiro, and Hugo Gamboa. "An Infrastructure-Free Magnetic-Based Indoor Positioning System with Deep Learning." Sensors 20, no. 22 (November 20, 2020): 6664. http://dx.doi.org/10.3390/s20226664.

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Infrastructure-free Indoor Positioning Systems (IPS) are becoming popular due to their scalability and a wide range of applications. Such systems often rely on deployed Wi-Fi networks. However, their usability may be compromised, either due to scanning restrictions from recent Android versions or the proliferation of 5G technology. This raises the need for new infrastructure-free IPS independent of Wi-Fi networks. In this paper, we propose the use of magnetic field data for IPS, through Deep Neural Networks (DNN). Firstly, a dataset of human indoor trajectories was collected with different smartphones. Afterwards, a magnetic fingerprint was constructed and relevant features were extracted to train a DNN that returns a probability map of a user’s location. Finally, two postprocessing methods were applied to obtain the most probable location regions. We asserted the performance of our solution against a test dataset, which produced a Success Rate of around 80%. We believe that these results are competitive for an IPS based on a single sensing source. Moreover, the magnetic field can be used as an additional information layer to increase the robustness and redundancy of current multi-source IPS.

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Adeyeye Oshin, Michael, and Nobaene Sehloho. "An Indoor Positioning System Using Multiple Methods and Tools." International Journal of Information, Communication Technology and Applications 4, no. 1 (May 1, 2018): 11–22. http://dx.doi.org/10.17972/ijicta20184134.

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With many different studies showing a growing demand for the development of indoor positioning systems, numerous positioning and tracking methods and tools are available for which can be used for mobile devices. Therefore, an interest is more on development of indoor positioning and tracking systems that are accurate and effective. Presented and proposed in this work, is an indoor positioning system. As opposed to an Ad-hoc Positioning System (APS), it uses a Wireless Mesh Network (WMN). The system makes use of an already existing Wi-Fi infrastructure technology. Moreover, the approach tests the positioning of a node with its neighbours in a mesh network using multi-hopping functionality. The positioning measurements used were the ICMP echos, RSSI and RTS/CTS requests and responses. The positioning method used was the trilateral technique, in combination with the idea of the fingerprinting method. Through research and experimentation, this study developed a system which shows potential as a positioning system with an error of about 2 m to 3 m. The hybridisation of the method proves an enhancement in the system though improvements are still required.

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Tabata, K., H. Konno, and M. Nakajima. "THE DESIGN OF WORKER'S BEHAVIOR ANALYSIS METHOD IN WORKPLACE USING INDOOR POSITIONING TECHNOLOGY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 127–31. http://dx.doi.org/10.5194/isprsannals-iii-4-127-2016.

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This study presents a method for analyzing workers’ behavior using indoor positioning technology and field test in the workplace. Recently, various indoor positioning methods, such as Wi-Fi, Bluetooth low energy (BLE), visible light communication, Japan’s indoor messaging system, ultra-wide band (UWB), and pedestrian dead reckoning (PDR), have been investigated. The development of these technologies allows tracking of movement of both people and/or goods in indoor spaces, people and/or goods behavior analysis is expected as one of the key technologies for operation optimization. However, when we use these technologies for human tracking, there are some problem as follows. 1) Many cases need to use dedicated facilities (e.g. UWB). 2) When we use smartphone as sensing device, battery depletion is one of the big problem (especially using PDR). 3) the accuracy is instability for tracking (e.g. Wi-Fi). Based on these matters, in this study we designed and developed an indoor positioning system using BLE positioning. And, we adopted smartphone for business use as sensing device, developed a smartphone application runs on android OS. Moreover, we conducted the field test of developed system at Itoki Corporation’s ITOKI Tokyo Innovation Center, SYNQA, office (Tokyo, Japan). Over 40 workers participated in this field test, and worker tracking log data were collected for 6 weeks. We analyzed the characteristics of the workers’ behavior using this log data as a prototyping.

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Tabata, K., H. Konno, and M. Nakajima. "THE DESIGN OF WORKER'S BEHAVIOR ANALYSIS METHOD IN WORKPLACE USING INDOOR POSITIONING TECHNOLOGY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 127–31. http://dx.doi.org/10.5194/isprs-annals-iii-4-127-2016.

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This study presents a method for analyzing workers’ behavior using indoor positioning technology and field test in the workplace. Recently, various indoor positioning methods, such as Wi-Fi, Bluetooth low energy (BLE), visible light communication, Japan’s indoor messaging system, ultra-wide band (UWB), and pedestrian dead reckoning (PDR), have been investigated. The development of these technologies allows tracking of movement of both people and/or goods in indoor spaces, people and/or goods behavior analysis is expected as one of the key technologies for operation optimization. However, when we use these technologies for human tracking, there are some problem as follows. 1) Many cases need to use dedicated facilities (e.g. UWB). 2) When we use smartphone as sensing device, battery depletion is one of the big problem (especially using PDR). 3) the accuracy is instability for tracking (e.g. Wi-Fi). Based on these matters, in this study we designed and developed an indoor positioning system using BLE positioning. And, we adopted smartphone for business use as sensing device, developed a smartphone application runs on android OS. Moreover, we conducted the field test of developed system at Itoki Corporation’s ITOKI Tokyo Innovation Center, SYNQA, office (Tokyo, Japan). Over 40 workers participated in this field test, and worker tracking log data were collected for 6 weeks. We analyzed the characteristics of the workers’ behavior using this log data as a prototyping.

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Bai, Lu, and Chenglie Du. "Wide-Band High-Gain DGS Antenna System for Indoor Robot Positioning." International Journal of Antennas and Propagation 2019 (January 20, 2019): 1–10. http://dx.doi.org/10.1155/2019/2102593.

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Based on multisource wireless signal fusion technology, the autonomous positioning systems of robots have been widely employed. How to design a compact compostable antenna array for indoor robot positioning is still a problem. In this study, we proposed a compact ultrathin antenna unit that effectively reduces the mutual coupling between any adjacent units, while covering most of the existing communication bands, including 2G/3G/4G/Wi-Fi, which will greatly reduce the size of the positioning antenna array. The proposed antenna system has been employed for positioning purpose with high-gain, wide-frequency band and limited size. It necessarily improves the accuracy of positioning signal from various unknown sources and finally accomplishes its autonomous positioning function.

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Selamat, M. H., and An Narzullaev. "WI-FI SIGNAL STRENGTH VS. MAGNETIC FIELDS FOR INDOOR POSITIONING SYSTEMS." Eurasian Journal of Mathematical and Computer Applications 2, no. 2 (2014): 122–33. http://dx.doi.org/10.32523/2306-6172-2014-2-2-122-133.

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Chen, Jian, Gang Ou, Ao Peng, Lingxiang Zheng, and Jianghong Shi. "A Hybrid Dead Reckon System Based on 3-Dimensional Dynamic Time Warping." Electronics 8, no. 2 (February 5, 2019): 185. http://dx.doi.org/10.3390/electronics8020185.

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In recent years, using smartphones for indoor positioning has become increasingly popular with consumers. This paper presents an integrated localization technique for inertial and magnetic field sensors to challenge indoor positioning without Wi-Fi signals. For dead-reckoning (DR), attitude angle estimation, step length calculation, and step counting estimation are introduced. Dynamic time warping (DTW) usually calculates the distance between the measured magnetic field and magnetic fingerprint in the database. For DR/Magnetic matching (MM), we creatively propose 3-dimensional dynamic time warping (3DDTW) to calculate the distance. Unlike traditional DTW, 3DDTW extends the original one-dimensional signal to a two-dimensional signal. Finally, the weighted least squares further improves indoor positioning accuracy. In the three different experimental scenarios—teaching building, study room, office building—DR/MM hybrid positioning accuracy is about 3.34 m.

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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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Zhang, Zhongfeng, Minjae Lee, and Seungwon Choi. "Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories." Sensors 21, no. 17 (August 27, 2021): 5776. http://dx.doi.org/10.3390/s21175776.

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In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.

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Ferry, Eugene, John O'Raw, and Kevin Curran. "A Context-Aware Mobility Indoor Positioning System." International Journal of Adaptive, Resilient and Autonomic Systems 6, no. 1 (January 2015): 25–47. http://dx.doi.org/10.4018/ijaras.2015010102.

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The need for location based services has dramatically increased within the past few years, especially with the popularity and capability of mobile device such as smart phones and tablets. The limitation of GPS for indoor positioning has seen an increase of indoor positioning based on Wireless Local Area Network 802.11. The authors demonstrate here a real world application of determining one's location with the Cisco Context-Aware Mobility which provides a Real Time Location System solution based on Wi-Fi. They detail their implementation of an Android application which communicates with the Cisco Context-Aware Mobility system to visually display the location of the mobile device. The application was tested in a production environment and limitations in the production environment along with the diagnostic capabilities of the Context-Aware Mobility were identified. The authors found that to obtain optimal accuracy, a device must be detected by four or more Access points so a recommended distribution for an indoor positioning system built on the Cisco context-aware mobility framework is for an Access Point to be placed every 12 – 20 linear meters.

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Sawada, Kensuke, Yuichi Hanada, and Shinichiro Mori. "User-installable Indoor Positioning System Using a Wi-Fi Beacon and PDR Module." Journal of Information Processing 24, no. 6 (2016): 843–52. http://dx.doi.org/10.2197/ipsjjip.24.843.

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Bonthu, Bhulakshmi, and M. Subaji. "An effective algorithm to overcome the practical hindrance for Wi-Fi based indoor positioning system." Open Computer Science 10, no. 1 (June 24, 2020): 117–23. http://dx.doi.org/10.1515/comp-2020-0010.

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AbstractIndoor tracking has evolved with various methods. The most popular method is using signal strength measuring techniques like triangulation, trilateration and fingerprinting, etc. Generally, these methods use the internal sensors of the smartphone. All these techniques require an adequate number of access point signals. The estimated positioning accuracy depends on the number of signals received at any point and precision of its signal (Wi-Fi radio waves) strength. In a practical environment, the received signal strength indicator (RSSI) of the access point is hindered by obstacles or blocks in the direct path or Line of sight. Such access points become an anomaly in the calculation of position. By detecting the anomaly access points and neglecting it during the computation of an indoor position will improve the accuracy of the positioning system. The proposed method, Practical Hindrance Avoidance in an Indoor Positioning System (PHA-IPS), eliminate the anomaly nodes while estimating the position, so then enhances the accuracy.

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Journal articles on the topic 'Wi-Fi Indoor positioning system'

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