Pitch angle sliding variance test method based on Mahony filter for zero-velocity detection

Abstract

The threshold judgment method is a comparatively simple and classic zero-velocity detection (ZVDT) method for zero-velocity update. To solve the problem with the existing threshold judgment method, where the ZVDT effect is critically diminished by sensor noise, motion state changes, and other factors, this paper proposes a pitch angle sliding variance test (PSVT) method based on Mahony filtering. First, a Mahony filtering algorithm is designed to merge and de-noise the original foot acceleration and angular velocity information to provide high-precision pitch angle information. Subsequently, based on the significant periodicity, isolation, and characteristics of foot pitch angle variation during pedestrian movement, a PSVT method is proposed for pedestrian ZVDT. Experimental results show that for the motions of different pedestrians in different scenes, the average single-step misjudgment rate in the zero-velocity interval of the proposed method is less than 10% overall. When compared with the generalized likelihood ratio test, the ZVDT effect is improved by approximately 30%. The method effectively improves the applicability and accuracy of ZVDT for pedestrian movement, and will be significant in improving the applicability, navigational accuracy, and practical use of pedestrian autonomous navigation systems.