WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the Signal Round Trip Time

Feng, Xu; Nguyen, Khuong An; Luo, Zhiyuan

doi:10.3390/rs14236052

Cited by 18 publications

(13 citation statements)

References 87 publications

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“…Existing wireless NLOS identification algorithms [ 3 , 15 ] are broadly classified into three categories based on the nature of their feature parameters. Distance-based methods utilize the difference in variance or probability density functions [ 15 ] of ranging distances in diverse environments for channel state identification.…”

Section: Related Workmentioning

confidence: 99%

“…However, commercial Global Satellite Navigation Systems (GNSSs) [ 1 ] are not designed for indoor location services due to severe interference from building structures. As a result, researchers have explored various techniques for indoor positioning, including fingerprint-matching methods based on Wireless Fidelity (Wi-Fi) [ 2 , 3 ], Bluetooth [ 4 ], or geomagnetism [ 5 ]; ranging positioning methods based on ultra-wideband (UWB) [ 6 , 7 ] and pseudo-satellite systems [ 8 ]; and angle-positioning methods [ 9 ] based on antenna array technology. Among them, UWB has emerged as a promising technology for accurate position estimation and synchronization control in harsh indoor environments.…”

Section: Introductionmentioning

confidence: 99%

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Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step Non-Line-of-Sight Identification

Yang,

Wang,

et al. 2024

Sensors

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The effective identification and mitigation of non-line-of-sight (NLOS) ranging errors are essential for achieving high-precision positioning and navigation with ultra-wideband (UWB) technology in harsh indoor environments. In this paper, an efficient UWB ranging-error mitigation strategy that uses novel channel impulse response parameters based on the results of a two-step NLOS identification, composed of a decision tree and feedforward neural network, is proposed to realize indoor locations. NLOS ranging errors are classified into three types, and corresponding mitigation strategies and recall mechanisms are developed, which are also extended to partial line-of-sight (LOS) errors. Extensive experiments involving three obstacles (humans, walls, and glass) and two sites show an average NLOS identification accuracy of 95.05%, with LOS/NLOS recall rates of 95.72%/94.15%. The mitigated LOS errors are reduced by 50.4%, while the average improvement in the accuracy of the three types of NLOS ranging errors is 61.8%, reaching up to 76.84%. Overall, this method achieves a reduction in LOS and NLOS ranging errors of 25.19% and 69.85%, respectively, resulting in a 54.46% enhancement in positioning accuracy. This performance surpasses that of state-of-the-art techniques, such as the convolutional neural network (CNN), long short-term memory–extended Kalman filter (LSTM-EKF), least-squares–support vector machine (LS-SVM), and k-nearest neighbor (K-NN) algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step Non-Line-of-Sight Identification

Yang,

Wang,

et al. 2024

Sensors

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“…Introduced by the IEEE 802.11n standard, CSI has received widespread attention in indoor positioning [5], [10], [18]- [20]. The system in [21] used a combined CNN and LSTM network for CSI fingerprinting.…”

Section: Related Workmentioning

confidence: 99%

“…Each of these measures has its own set of strengths and weaknesses. For instance, RTT excels in clear line-of-sight scenarios but lacks stability over a long time period, while RSS performs optimally in heavily attenuated non-line-of-sight conditions; CSI has the potential for fine-grained positioning, but is not yet widely supported by most hardware [10].…”

Section: Introductionmentioning

confidence: 99%

A Wi-Fi RSS-RTT Indoor Positioning Model Based on Dynamic Model Switching Algorithm

Feng,

Nguyen,

Luo

2024

J. Ind. Sea. Pos. Nav.

Self Cite

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The advances in WiFi technology have encouraged the development of numerous indoor positioning systems. However, their performance varies significantly across different indoor environments, making it challenging in identifying the most suitable system for all scenarios. To address this challenge, we propose an algorithm that dynamically selects the most optimal WiFi positioning model for each location. Our algorithm employs a Machine Learning weighted model selection algorithm, trained on raw WiFi RSS, raw WiFi RTT data, statistical RSS & RTT measures, and Access Point line-of-sight information. We tested our algorithm in four complex indoor environments, and compared its performance to traditional WiFi indoor positioning models and state-of-the-art stacking models, demonstrating an improvement of up to 1.8 meters on average.

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“…SVM, NN, and Multilayer Perception (MLP) are employed to classify the NLOS/LOS signals. For example, Xu [44] identified LOS signals by using Wi-Fi RTT data with an accuracy of 98%. If RSSI is too high or too low in a particular environment, this phenomenon is likely instructed under the LOS or NLOS condition [45].…”

Section: Related Workmentioning

confidence: 99%

A Novel Device-Free Positioning Method Based on Wi-Fi CSI with NLOS Detection and Bayes Classification

Zheng

Chen

et al. 2023

Remote Sensing

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Device-free wireless localization based on Wi-Fi channel state information (CSI) is an emerging technique that could estimate users’ indoor locations without invading their privacy or requiring special equipment. It deduces the position of a person by analyzing the influence on the CSI of Wi-Fi signals. When pedestrians block the signals between the transceivers, the non-line-of-sight (NLOS) transmission occurs. It should be noted that NLOS has been a significant factor restricting the device-free positioning accuracy due to signal reduction and abnormalities during multipath propagation. For this problem, we analyzed the NLOS effect in an indoor environment and found that the position error in the LOS condition is different from the NLOS condition. Then, two empirical models, namely, a CSI passive positioning model and a CSI NLOS/LOS detection model, have been derived empirically with extensive study, which can obtain better robustness identified results in the case of NLOS and LOS conditions. An algorithm called SVM-NB (Support Vector Machine-Naive Bayes) is proposed to integrate the SVM NLOS detection model with the Naive Bayes fingerprint method to narrow the matching area and improve position accuracy. The NLOS identification precision is better than 97%. The proposed method achieves localization accuracy of 0.82 and 0.73 m in laboratory and corridor scenes, respectively. Compared to the Bayes method, our tests showed that the positioning accuracy of the NLOS condition is improved by 28.7% and that of the LOS condition by 26.2%.

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WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the Signal Round Trip Time

Cited by 18 publications

References 87 publications

Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step Non-Line-of-Sight Identification

Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step Non-Line-of-Sight Identification

A Wi-Fi RSS-RTT Indoor Positioning Model Based on Dynamic Model Switching Algorithm

A Novel Device-Free Positioning Method Based on Wi-Fi CSI with NLOS Detection and Bayes Classification

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