Deep contrastive learning based UWB AOA localization method in NLOS environments

Jun Zhu

doi:10.1117/12.3026415

8 April 2024 Deep contrastive learning based UWB AOA localization method in NLOS environments

Jun Zhu

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130901L (2024) https://doi.org/10.1117/12.3026415
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

With the development of the Internet of Things era, the quality of various services in daily life is improving. High-precision positioning capabilities are needed in various industries, such as goods tracking in the factory logistics industry, robot delivery in shopping malls and restaurants, and special population management in hospitals and schools. Due to its large frequency loan and narrowband pulse characteristics, Ultra-wide band (UWB) technology has a strong resolution in both time and space dimensions, so the centimeter-level positioning capability based on UWB is recognized and adopted by various industries. However, in the actual use of UWB, the Non-Line-of-Sight (NLOS) problem is a difficult issue to solve, which greatly affects the positioning experience. This paper proposes an Angle of Arrival (AOA) based UWB-positioning framework using a deep contrastive learning network, referred to as AUCNet. In AUCNet, the Channel Impulse Response (CIR) data of multiple antennas are enhanced and aligned, and then a deep convolutional network and a multi-channel attention mechanism network are used to identify NLOS scenarios. Finally, based on the NLOS identification results and range-angle information, we use a weighted AOA positioning method to solve the final positioning location. We conduct extensive experiments in real sites to verify NLOS identification accuracy, average positioning error, and deployment cost of several positioning methods. At the same time, we conduct an ablation experiment on the deep learning network structure of AUCNet. The experimental results show that AUCNet achieves an average positioning error improvement of 5.1 cm and an NLOS identification accuracy rate improvement of 8.5%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Zhu "Deep contrastive learning based UWB AOA localization method in NLOS environments", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130901L (8 April 2024); https://doi.org/10.1117/12.3026415

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KEYWORDS

Non line of sight propagation

Interpolation

Antennas

Network architectures

Ranging

Receivers

Deep learning

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