A Deep-Learning-Based Self-Calibration Time-Reversal Fingerprinting Localization Approach on Wi-Fi Platform

Zheng, Lili; Hu, Bin‐Jie; Qiu, Jinguang; Cui, Manman

doi:10.1109/jiot.2020.2981723

Cited by 31 publications

(18 citation statements)

References 44 publications

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“…Song et al [18] employed an SAE for dimension reduction of RSS data and combined it with a onedimensional CNN to improve the localization performance. In [30], a time-reversal fingerprinting localization method with RSS calibration was proposed. The method used the amplitude-AE and phase-AE to calibrate the online RSS measurements.…”

Section: Related Workmentioning

confidence: 99%

Wi‐Fi Fingerprint‐Based Indoor Mobile User Localization Using Deep Learning

Bai

Sun

Meng

et al. 2021

Wireless Communications and Mobile Computing

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In recent years, deep learning has been used for Wi-Fi fingerprint-based localization to achieve a remarkable performance, which is expected to satisfy the increasing requirements of indoor location-based service (LBS). In this paper, we propose a Wi-Fi fingerprint-based indoor mobile user localization method that integrates a stacked improved sparse autoencoder (SISAE) and a recurrent neural network (RNN). We improve the sparse autoencoder by adding an activity penalty term in its loss function to control the neuron outputs in the hidden layer. The encoders of three improved sparse autoencoders are stacked to obtain high-level feature representations of received signal strength (RSS) vectors, and an SISAE is constructed for localization by adding a logistic regression layer as the output layer to the stacked encoders. Meanwhile, using the previous location coordinates computed by the trained SISAE as extra inputs, an RNN is employed to compute more accurate current location coordinates for mobile users. The experimental results demonstrate that the mean error of the proposed SISAE-RNN for mobile user localization can be reduced to 1.60 m.

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Section: Related Workmentioning

confidence: 99%

Wi‐Fi Fingerprint‐Based Indoor Mobile User Localization Using Deep Learning

Bai

Sun

Meng

et al. 2021

Wireless Communications and Mobile Computing

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“…LBS is based on the network positioning method adopting machine learning techniques to estimate the locations of mobile devices by RSSIs from networks. Firstly, these methods utilize mobile devices to detect and collect data including coordinates of GPS and RSSIs from cellular network or Wi-Fi network and store them in the fingerprinting database [14][15][16]. Secondly, machine learning models are employed to capture the correlation between GPS coordinates and RSSIs.…”

Section: Related Workmentioning

confidence: 99%

Gated Recurrent Unit with RSSIs from Heterogeneous Network for Mobile Positioning

Wang

Guo

2021

Mobile Information Systems

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Recently, research studies on Location-Based Services (LBSs) based on networks including cellular network and Wi-Fi network have gradually become popular. Received Signal Strength Indicators (RSSIs) from the network can be detected and collected by mobile devices to estimate the locations without adopting the Global Positioning System (GPS). Previous research studies utilized the RSSIs of only cellular network or only Wi-Fi network to estimate location, which leads to a two-fold predicament involving error limits of cellular network-based methods and environmental constraints of Wi-Fi network-based methods. In addition, accommodating a highly temporal dependence of RSSI series data, this paper proposed a mobile positioning system based on Gated Recurrent Unit (GRU) with RSSIs from the heterogeneous network. GRU learns the temporal correlation of RSSIs and the relationship between RSSIs and GPS coordinates to estimate the locations of mobile devices. A large number of real experiments have been carried out to verify the performance of the proposed method, and experimental results demonstrate that the proposed method has lower errors (i.e., 5.86 m and 75% of errors within 4 m) compared with Neural Network (NN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM).

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“…WOA algorithm is combined with BP neural network, and WOA algorithm is used to adjust the parameters of BP neural network. Then the RSSI value data and corresponding parameters collected at different distances are taken as the input value of BP neural network, and the coordinates of unknown nodes are taken as the output value of BP neural network, so as to establish WOA-BP neural network model and complete the node positioning [21][22][23][24][25][26].…”

Section: Using Woa-bp To Optimize Indoor Environment Attenuation Modelmentioning

confidence: 99%

A Node Localization Algorithm based on WoaBp Optimization

Lang¹,

Sun²,

Jun³

et al. 2021

IJWMT

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With the rapid development of 5G technology, the era of interconnection of all things has arrived. At the same time, a variety of hardware and software are getting more and more location information through sensors, and the accuracy of location information is increasingly important. Because traditional positioning relies on satellite signals, it achieves good results outdoors without obstruction, but indoors, due to the obstruction of various walls, such as Beidou satellite navigation system and U.S. Global Positioning System, it is difficult to meet the accuracy requirements for indoor positioning. Therefore, how to improve the positioning accuracy of indoor nodes has become a research hotspot in the field of wireless sensor. In order to improve the indoor positioning accuracy, this paper combines artificial neural network, intelligent optimization algorithm and node positioning to improve the accuracy of indoor positioning. One of the essences of the neural network is to solve the regression problem. Through the analysis of indoor node positioning, it can be concluded that the accuracy of distance-based positioning method lies in finding the relationship between signal strength and distance value. Therefore, the neural network can be used to regression analysis of signal strength and distance value and generate related models. In order to further improve the accuracy and stability of indoor node positioning, a method combining whale optimization algorithm with neural network is proposed. By using the whale optimization algorithm to find the optimal parameters of the neural network model, the training accuracy and speed of the neural network are improved. Then, using the excellent fitting ability of the neural network, the mapping relationship between RSSI value and distance value of indoor nodes is fitted, and the corresponding regression analysis model is generated, which can minimize the noise problem caused by abnormal signal attenuation and reduce the indoor positioning error. Finally, the data is processed by the neural network to get the parameters needed in the positioning algorithm. The experimental results show that the node positioning model based on the optimized neural network and the single optimization algorithm has significantly improved the positioning accuracy and stability.

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A Deep-Learning-Based Self-Calibration Time-Reversal Fingerprinting Localization Approach on Wi-Fi Platform

Cited by 31 publications

References 44 publications

Wi‐Fi Fingerprint‐Based Indoor Mobile User Localization Using Deep Learning

Wi‐Fi Fingerprint‐Based Indoor Mobile User Localization Using Deep Learning

Gated Recurrent Unit with RSSIs from Heterogeneous Network for Mobile Positioning

A Node Localization Algorithm based on WoaBp Optimization

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