Optimization of indoor vehicle ultra-wideband 3D localization using graph attention networks

Ultra-wideband technology is recognized as a low-cost and interference-resistant method for indoor vehicle localization, crucial for advancing intelligent transportation systems. Within this domain, the time difference of arrival algorithm finds extensive application. However, the accuracy of indoor three-dimensional (3D) localization is significantly affected by multipath propagation and non-line-of-sight deviations. To improve localization accuracy, this study proposes an innovative method utilizing the graph attention network framework. This method extracts relevant features and employs attention mechanisms to differentially weight individual nodes; it adeptly captures crucial spatial relationships among nodes, thereby mitigating the effects of multipath propagation and non-line-of-sight deviations, ultimately achieving finer indoor vehicle localization. Experimental results from tests on line-of-sight and non-line-of-sight data sets illustrate the significant improvement in indoor vehicle 3D localization accuracy afforded by the proposed graph attention network-based method. Compared to the commonly used built-in error-state Kalman filter algorithm in commercial ultra-wideband applications and alternative deep learning algorithms, the proposed method achieves error reductions ranging from 60.58% to 84.16% in line-of-sight environments and from 65.59% to 95.21% in non-line-of-sight environments.