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- W4387527410 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" @default.
- W4387527410 created "2023-10-12" @default.
- W4387527410 creator A5042226175 @default.
- W4387527410 date "2023-10-11" @default.
- W4387527410 modified "2023-10-12" @default.
- W4387527410 title "UWB indoor localization using deep aggregation network" @default.
- W4387527410 doi "https://doi.org/10.1117/12.3004589" @default.
- W4387527410 hasPublicationYear "2023" @default.
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