Performance Comparison of Indoor Fingerprinting Techniques Based on Artificial Neural Network

Soro, Bedionita; Lee, Chae-Woo

doi:10.1109/tencon.2018.8650230

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…The results of the corridor experimentation are presented in Table 2. The experimentation results compared to those in [21] show that the proposed method has reduced the positioning error to around 46%.…”

Section: Experimentation Results and Analysismentioning

confidence: 99%

“…The proposed algorithm achieved around 99.64% on floor classification and the location error was between 4 m and 5 m with minimum error 4.15 m which is higher than the positioning error achieved by KNN as shown in Table 4. In addition, the autoencoder model presented in [9,21] applied to this database merely achieved 60% on floor classification. The same observation has been proved as in the first option.…”

Section: Experimentation Results and Analysismentioning

confidence: 99%

“…The main aim in this work is to distinguish anomaly reading from correct reading and not to address the RSSI fluctuation. Also, methods exploiting the ANN to mitigate the Wi-Fi RSSI fluctuation have been proposed [9,18,19,20,21].…”

Section: Related Workmentioning

confidence: 99%

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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization

Soro

Lee

2019

Sensors

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The performance of an Artificial Neural Network (ANN)-based algorithm is subject to the way the feature data is extracted. This is a common issue when applying the ANN to indoor fingerprinting-based localization where the signal is unstable. To date, there is not adequate feature extraction method that can significantly mitigate the influence of the receiver signal strength indicator (RSSI) variation that degrades the performance of the ANN-based indoor fingerprinting algorithm. In this work, a wavelet scattering transform is used to extract reliable features that are stable to small deformation and rotation invariant. The extracted features are used by a deep neural network (DNN) model to predict the location. The zeroth and the first layer of decomposition coefficients were used as features data by concatenating different scattering path coefficients. The proposed algorithm has been validated on real measurements and has achieved good performance. The experimentation results demonstrate that the proposed feature extraction method is stable to the RSSI variation.

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Section: Experimentation Results and Analysismentioning

confidence: 99%

Section: Experimentation Results and Analysismentioning

confidence: 99%

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A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization

Soro

Lee

2019

Sensors

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“…Roughly, those methods can be classified into two categories, namely, the received signal feature-based methods, and the propagation time-based methods [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38]. The received signal strength indicator (RSSI) fingerprint is the most widely adopted feature to indicate position information in favor of its simplicity and accessibility [15,16,17,18,19,20,21,22,23,24,25]. Generally, the fingerprint based schemes are conducted in two phases, i.e., an offline phase followed by an online phase.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is not required to know the exact positions of all APs, which leads to the advantage of deployment flexibility. In the online phase, the target position is estimated by comparing the current RSSI vector to the fingerprint database using the matching algorithms such as the maximum likelihood [15], k-nearest neighbors [16], support vector machine [17], random forest [18], Bayesian network [19], Gaussian process [20] and an artificial neural network [21]. The key system parameters, such as the number of APs, the density of RPs and the time span of signal collection determine the positioning precision.…”

Section: Introductionmentioning

confidence: 99%

A Wi-Fi-Based Wireless Indoor Position Sensing System with Multipath Interference Mitigation

Xie

Jiang

Zhao

et al. 2019

Sensors

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Wi-Fi-based indoor position sensing solutions have the advantages of easy integration in mobile phones and low cost by using existing Wi-Fi access points. The mainstream methods are commonly based on the received signal strength indicator (RSSI), which suffers from multipath interference in complicated indoor environments. Through the in-depth analysis of the multipath interference, an RSSI-assisted time difference of arrival (TDoA) method is proposed for Wi-Fi-based indoor position sensing in this work. The key idea is to compensate for the multipath interference in the received signals based on the coarse estimation using RSSI and TDoA calculation. A prototype system has been implemented to validate the proposed method. Experimental results have demonstrated the effectiveness of the proposed method, especially for handling the multipath interference with small propagation delay difference. Experimental results show that the indoor position sensing system can achieve a 90th percentile error of 0.3 m. The proposed method can also achieve moderate computational complexity and moderate real-time performance compared to other methods.

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A survey of deep learning approaches for WiFi-based indoor positioning

Feng

Nguyen

Luo

2021

Journal of Information and Telecommunication

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One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments.

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Performance Comparison of Indoor Fingerprinting Techniques Based on Artificial Neural Network

Cited by 8 publications

References 6 publications

A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization

A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization

A Wi-Fi-Based Wireless Indoor Position Sensing System with Multipath Interference Mitigation

A survey of deep learning approaches for WiFi-based indoor positioning

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