CNN-BiLSTM-ATTENTION: A Novel Neural Network with Attention Mechanism for NLOS Identification of UWB Signal

Yang, Yang; Ke, Han; Gan, Wang; Deng, Zhongliang

doi:10.1109/icc59986.2023.10421448

2023 3rd International Conference on Intelligent Communications and Computing (ICC) 2023

DOI: 10.1109/icc59986.2023.10421448

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CNN-BiLSTM-ATTENTION: A Novel Neural Network with Attention Mechanism for NLOS Identification of UWB Signal

Yang Yang,

Han Ke,

Wang Gan

et al.

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2024

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Cited by 2 publications

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UWB Wireless Positioning Method Based on LightGBM

Cui,

Li,

et al. 2024

Wireless Pers Commun

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In the ultra-wideband indoor positioning sceneraio, the non-line of sight (NLOS) propagation may be caused by obstacles, which may lead to the deviation of ranging value and affect the positioning precision. Therefore, we propose a NLOS identification and error regression positioning algorithm based on light gradient boosting machine (LightGBM). Firstly, ReliefF algorithm combined with Spearman correlation coefficient is used to analyze the feature correlation, and eight channel features such as total channel impulse response power and standard deviation of noise are selected as NLOS identification features. Then, we adopt genetic algorithm to optimize the hyperparameters of LightGBM for NLOS identification. On this basis, the proposed error regression model based on convolutional neural network (CNN) combined with LightGBM is used to correct the ranging results, so as to achieve highprecision positioning. Through the verification on the public dataset, the NLOS identification accuracy reached 91.8%, and the positioning precision is improved by 45cm after correcting the ranging results.

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UWB Wireless Positioning Method Based on LightGBM

Cui,

Li,

et al. 2024

Wireless Pers Commun

View full text Add to dashboard Cite

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1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System

Wang,

Chen,

Liu

et al. 2024

Applied Sciences

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Ultra-Wideband (UWB) technology is crucial for indoor localization systems due to its high accuracy and robustness in multipath environments. However, Non-Line-of-Sight (NLoS) conditions can cause UWB signal distortion, significantly reducing positioning accuracy. Thus, distinguishing between NLoS and LoS scenarios and mitigating positioning errors is crucial for enhancing UWB system performance. This research proposes a novel 1D-ConvLSTM-Attention network (1D-CLANet) for extracting UWB temporal channel impulse response (CIR) features and identifying NLoS scenarios. The model combines the convolutional neural network (CNN) and Long Short-Term memory (LSTM) architectures to extract temporal CIR features and introduces the Squeeze-and-Excitation (SE) attention mechanism to enhance critical features. Integrating SE attention with LSTM outputs boosts the model’s ability to differentiate between various NLoS categories. Experimental results show that the proposed 1D-CLANet with SE attention achieves superior performance in differentiating multiple NLoS scenarios with limited computational resources, attaining an accuracy of 95.58%. It outperforms other attention mechanisms and the version of 1D-CLANet without attention. Compared to advanced methods, the SE-enhanced 1D-CLANet significantly improves the ability to distinguish between LoS and similar NLoS scenarios, such as human obstructions, enhancing overall recognition accuracy in complex environments.

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CNN-BiLSTM-ATTENTION: A Novel Neural Network with Attention Mechanism for NLOS Identification of UWB Signal

Cited by 2 publications

References 8 publications

UWB Wireless Positioning Method Based on LightGBM

UWB Wireless Positioning Method Based on LightGBM

1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System

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