Robust LOS/NLOS Identification for UWB Signals Using Improved Fuzzy Decision Tree Under Volatile Indoor Conditions

Zhu, Fei; Yu, Kegen; Lin, Yiruo; Wang, Changyang; Wang, Jin; Chao, M.N.

doi:10.1109/tim.2023.3276521

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…DTs [ 7 ] offer interpretability, handle non-linearity, require no feature scaling, accommodate both numerical and categorical data, and provide feature importance, making them advantageous for classification tasks. As shown in Figure 4 b, in this paper, combining the DT with the time domain, energy, and proposed new CIR features extracted from the key UWB communication stages (ENS, SRS) can quickly and accurately identify and pre-extract data with typical LOS or NLOS channel characteristics.…”

Section: Proposed Methodsmentioning

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: Proposed Methodsmentioning

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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NLOS Identification for UWB Positioning Based on IDBO and Convolutional Neural Networks

Kong

2023

IEEE Access

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Ultra-wideband (UWB) is regarded as the technology with the most potential for precise indoor location due to its centimeter-level ranging capabilities, good time resolution, and low power consumption. However, Because of the presence of non-line-of-sight (NLOS) error, the accuracy of UWB localization deteriorates significantly in harsh and volatile indoor conditions. Therefore, identifying NLOS conditions is crucial to enhancing the accuracy of UWB location. This paper proposes a convolutional neural network (CNN) classification method based on an improved Dung Beetle Optimizer (DBO). Firstly, based on the standard DBO, the Circle chaotic mapping, non-uniform Gaussian variational strategy, and multi-stage perturbation strategy are used to optimize the exploration capability and enhance the performance of original DBO method, the superiority-seeking ability of IDBO is demonstrated by testing 23 benchmark functions. In addition, based on the IDBO algorithm, we propose an IDBO-CNN classification model, with the help of IDBO, the accuracy of NLOS identification is improved by adjusting the hyperparameters of CNN to be closer to the optimal solution. Experiments conducted on the open-source dataset demonstrate that IDBO-CNN is capable of achieve the desired effect. In comparison to the conventional CNN approach, the F1-score achieved by IDBO-CNN is enhanced by 3.31%, which demonstrates that IDBO-CNN has superior identification accuracy.

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Accurate Multiclass NLOS Channels Identification in UWB Indoor Positioning System-Based Deep Neural Network

Fahem Majeed,

Arsat,

Ariff Baharudin

et al. 2024

IEEE Access

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Robust LOS/NLOS Identification for UWB Signals Using Improved Fuzzy Decision Tree Under Volatile Indoor Conditions

Cited by 7 publications

References 43 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

NLOS Identification for UWB Positioning Based on IDBO and Convolutional Neural Networks

Accurate Multiclass NLOS Channels Identification in UWB Indoor Positioning System-Based Deep Neural Network

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