ToF-Based NLoS Indoor Tracking With Adaptive Ranging Error Mitigation

Accurate indoor localization remains a significant challenge, primarily due to multipath and non-line-of-sight (NLoS) propagation conditions in complex indoor environments. Traditional localization methods often rely on oversimplified assumptions or require prior knowledge of channel or ranging error statistics. Unfortunately, these approaches overlook the environment/location-dependent nature of the ranging error, e.g., highly dynamic and unpredictable, resulting in sub-optimal performances in real-world settings. To address these challenges, we introduce a novel Bayesian tracking framework that simultaneously tracks the statistics of ranging errors and target's location for fine-grained ranging error mitigation, without the need for prior knowledge of the channel or environment. The proposed method characterizes the distribution of ranging error using mixture distributions with dynamically updated parameters. A hidden Markov model (HMM) is employed to track the sight condition (i.e. LoS or NLoS) of the propagation channel and adjust the parameters of the ranging error model online. Our proposed framework focuses on 802.11 range-based localization systems and aims to deliver general-purpose localization services where sub-meter level accuracy is sufficient. Experimental evaluations conducted across two real-world indoor scenarios demonstrate that the proposed method significantly improves localization accuracy to 1 meter in challenging multipath and NLoS environments, outperforming existing techniques while maintaining similar computation complexity.