Deep neural network‐based adaptive zero‐velocity detection for pedestrian navigation system

Abstract

The zero-velocity update (ZUPT) method is an effective way to reduce accumulated velocity errors of pedestrian navigation systems (PNSs). For a typical scheme, a stance phase detection module based on a fixed threshold is used to trigger the ZUPT algorithm. However, the detector is not robust enough for dynamic gait speeds. The false detection will degrade the navigation performance. In this letter, to improve the stance phase detector, the adaptive zero-velocity detection problem is cast under dynamic gait speeds as a sequential threshold of a traditional detector inferring problem and a zero-velocity detection framework proposed by combining a deep neural network with a traditional binary gait phase detector. Sufficient experimental results show that the proposed method outperforms other discussed learning-based methods taking into account the trade-off among model performance, structure, and size. Compared with the traditional method with a fixed threshold, the real-world high-dynamic positioning experiments show that this proposed method reduces the root mean squared error (RMSE) of absolute distance error by 48.7%, RMSE of start-end error by 12.5%, and average RMSE of position error by 19.2%.