UWB indoor localization using deep aggregation network

Jun Zhu

doi:10.1117/12.3004589

11 October 2023 UWB indoor localization using deep aggregation network

Jun Zhu

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128001M (2023) https://doi.org/10.1117/12.3004589
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Ultra-wide band (UWB) technology has become a mainstream technology of high-precision IoT positioning. Due to its large frequency band and narrow pulse characteristics, UWB signals can well cope with multipath effects, and are currently widely used in personnel positioning in factories, goods positioning in warehouses and other scenarios. However, due to the non-line of sight (NLOS) problem, its positioning performance will be greatly reduced in the actual scene. The existing schemes are mainly solved by threshold method and feature method, but the effect is not ideal. This paper proposes a unified UWB indoor positioning framework based on deep aggregation network, called UANet. In UANet, phase and amplitude information are obtained by processing channel impulse response (CIR), and then a deep aggregation network integrates CNN and Transformer to identify NLOS scenes. Finally, weighted least squares method (WLS) is applied to complete high-precision positioning of indoor scenes. We conduct extensive experiments in real scenes, and the average positioning accuracy of different depth learning models and traditional UWB positioning methods is compared. The experimental results show that compared with traditional methods, the proposed UANet achieves an average positioning error improvement of 6.4 cm

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Zhu "UWB indoor localization using deep aggregation network", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128001M (11 October 2023); https://doi.org/10.1117/12.3004589

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