Multi‐layer neural network for received signal strength‐based indoor localisation

Dai, Huan; Ying, Wenhao; Xu, Jingdong

doi:10.1049/iet-com.2015.0469

Cited by 86 publications

(46 citation statements)

References 23 publications

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“…Using 207 RPs for training and 50 random points for validation, the accuracy of this model is 2.82±0.11 m, which is comparable with that of simple KNN algorithm. In order to achieve better performance, multi-layer feed-forward neural network with 3 hidden layers was investigated [20]. MLNN is designed with 3 sections: RSSI transformation section, RSSI denoising section and localization section with the boosting method to tune the network parameters for misclassification correction.…”

Section: Related Workmentioning

confidence: 99%

“…ANN is a suitable and reliable solution for its ability to approximate high dimension and highly nonlinear models [5]. Recently, several ANN localization solutions, such as multilayer perceptron (MLP) [18], robust extreme learning machine (RELM) [19], multi-layer neural network (MLNN) [20], convolutional neural network (CNN) [21], etc., have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…2 The proposed models are tested in two different datasets including an in-house measurement dataset and the published dataset UJIIndoorLoc [32]. The accuracy is compared not only with the other neural network methods, i.e., MLP [18] and MLNN [20], but also some popular conventional methods, i.e., RADAR [12], SRL-KNN [22], Kernel method [9] and Kalman filter [23]. 3 Three challenges of WiFi indoor localization, i.e., spatial ambiguity, RSSI instability and RSSI short collecting time per location are discussed and addressed.…”

Section: Introductionmentioning

confidence: 99%

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Recurrent Neural Networks for Accurate RSSI Indoor Localization

Hoang

Yuen

Dong

et al. 2019

IEEE Internet Things J.

274

139

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This paper proposes recurrent neural networks (RNNs) for WiFi fingerprinting indoor localization. Instead of locating a mobile user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the correlation among the received signal strength indicator (RSSI) measurements in a trajectory. To enhance the accuracy among the temporal fluctuations of RSSI, a weighted average filter is proposed for both input RSSI data and sequential output locations. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional RNN (BiRNN), bidirectional LSTM (BiLSTM) and bidirectional GRU (BiGRU) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of 0.75 m with 80% of the errors under one meter, which outperforms KNN algorithms and probabilistic algorithms by approximately 30% under the same test environment.Index Terms-Received signal strength indicator, WiFi indoor localization, recurrent neuron network, long short-term memory, fingerprint-based localization.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recurrent Neural Networks for Accurate RSSI Indoor Localization

Hoang

Yuen

Dong

et al. 2019

IEEE Internet Things J.

274

139

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show abstract

“…Wu et al [60] combined SVM and particle filter. They classified the data in the fingerprint database as per the known position by SVM and used Pedestrian Dead Reckoning to achieve a positioning match, and optimized the data by particle filter.…”

Section: Deterministic Positioning Algorithmmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

185

123

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Abstract:The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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“…Despite its low complexity, the accuracy of KNN is unstable due to the substantial fluctuations of Wi-Fi signals [8]- [10]. On the other hand, machine learning algorithms such as multilayer perceptron (MLP) [11], robust extreme learning machine (RELM) [12], multi-layer neural network (MLNN) [13], convolutional neural network (CNN) [14], etc., provide highest accuracy [15], [16] while most of them are sophisticated in nature and require extremely high computational complexity in training [10]. In contrast, probabilistic methods are based on the statistical inference between the measured signal and the stored fingerprints through Bayes rule [17].…”

Section: Introductionmentioning

confidence: 99%

Semi-Sequential Probabilistic Model for Indoor Localization Enhancement

et al. 2020

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This paper proposes a semi-sequential probabilistic model (SSP) that applies an additional short term memory to enhance the performance of the probabilistic indoor localization. The conventional probabilistic methods normally treat the locations in the database indiscriminately. In contrast, SSP leverages the information of the previous position to determine the probable location since the user's speed in an indoor environment is bounded and locations near the previous one have higher probability than the other locations. Although the SSP utilizes the previous location information, it does not require the exact moving speed and direction of the user. On-site experiments using the received signal strength indicator (RSSI) and channel state information (CSI) fingerprints for localization demonstrate that SSP reduces the maximum error and boosts the performance of existing probabilistic approaches by 25% -30%.Index Terms-Received signal strength indicator (RSSI), channel state information (CSI), WiFi indoor localization, probabilistic approach based localization.

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Multi‐layer neural network for received signal strength‐based indoor localisation

Cited by 86 publications

References 23 publications

Recurrent Neural Networks for Accurate RSSI Indoor Localization

Recurrent Neural Networks for Accurate RSSI Indoor Localization

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Semi-Sequential Probabilistic Model for Indoor Localization Enhancement

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