Achieving Cross-Domain NLOS Localization via Edge-Assisted Semi-Supervised Learning

The localization of coal mine robots (CMRs) serves as the foundation for intelligent mines. Despite various approaches proposed by academia and industry, existing range-based localization methods generally encounter the non-line-of-sight (NLOS) problem, leading to severe accuracy degradation in complicated underground mines. Facing the above challenge, this paper proposes an edge-assisted cross-domain NLOS localization (CrossDNL) framework based on semi-supervised learning. Specifically, we analyze the channel impulse responses (CIRs) of ultra-wideband (UWB) to identify the multi-channel NLOS conditions and further mitigate ranging errors via the deep neural network (DNN). To achieve reliable localization across various scenarios, CrossDNL adopts an edge-assisted semi-supervised learning architecture that enables it to optimize DNN models by self-training on the edge server and achieve the real-time service on the CMR. Besides, we propose a range-based error state Kalman filter (ESKF) scheme to improve the localization performance. We implement CrossDNL on the CMR and the edge server and evaluate it in various scenarios. Results show that CrossDNL can achieve a 14cm localization error in real time, outperforming the latest solutions by more than 15%.