Multiple Hypothesis Testing Approach to Pedestrian INS with Map-Matching

Abstract

Inertial sensors became wearable/portable with the advances in sensing and computing technologies in the last two decades. Captured motion data can be used to build a pedestrian inertial navigation system (INS); however, time-variant bias and noise characteristics of low-cost sensors cause severe errors in positioning. To overcome the growing errors of so-called dead-reckoning (DR) solution, this research adopts a robust pedestrian INS based on Kalman Filter (KF) with zero-velocity update (ZUPT) aid. Despite accurate traveled distance estimates, obtained trajectories diverge from actual paths because of the heading estimation errors. In the absence of external corrections (e.g., GPS, UWB), map information is commonly employed to eliminate position drift; therefore, INS solution is fed into a higher level map-matching filter for further corrections. Unlike common Particle Filter (PF) map-matching, we implicitly model map by rasterizing the floor plans that reduces the computational burden of PF. Second major usage of the introduced approach, which makes the Bayesian estimation cycle non-recursive by serving as a constant spatial prior in the designed filter, is to generate probabilities for a self-initialization method referred to as the Multiple Hypothesis Testing (MHT). Extracted scores update hypothesis probabilities in a cumulative manner and hypothesis with maximum probability provides the correct initial location and heading. Qualitative and quantitative experimental results show feasible trajectories, and negligible return-to-start and stride errors that validates the success of the proposed approach.