A Novel Adaptive Zero-Velocity Detector for Inertial Pedestrian Navigation Based on Optimal Interval Estimation

In the foot-mounted inertial pedestrian navigation system, the zero-velocity update (ZUPT) algorithm is an efficient way to bound the inertial error propagation. Therefore, a reliable and accurate zero-velocity detector (ZVD) that adapts to all kinds of locomotion and scenarios plays a vital role in achieving high-precision and long-term pedestrian navigation. The classical threshold-based ZVDs are susceptible to failures during dynamic locomotion due to the fixed threshold. Recent machine-learning-based ZVDs need a huge amount of data to support the model training and their generalization is limited in new testing scenarios. In this paper, we propose a novel adaptive ZVD using the optimal interval estimation. Two filters are used to process the angular rate, aiming at determining a gait cycle. In a gait cycle, the acceleration is mapped to the search space by a special convex function. Based on the features of the data in the search space, a zero-velocity benchmark is calculated for the following interval estimation. The zero-velocity benchmark and the hierarchical iterative search are used to estimate the optimal zero-velocity interval (ZVI). The experiments demonstrate the effectiveness and adaptability of this novel ZVD.