Abstract: To address the issues of missed and false detections in gait detection algorithms under complex motion modes and varying device carrying postures, an adaptive gait detection algorithm integrating a Mamba neural network and a finite state machine(FSM) was proposed. A classification model based on the Mamba network was constructed to jointly identify pedestrian motion modes and smartphone carrying postures. Furthermore, this algorithm fused an adaptive-threshold FSM, an angular velocity backtracking mechanism,and a gravity projection-based non-rigid coupling denoising strategy to achieve the stable triggering of gait events under complex carrying conditions, thereby further improving the positioning accuracy and robustness of pedestrian dead reckoning(PDR) in complex scenarios. Experimental results demonstrate that the proposed algorithm achieves high gait detection accuracy under various motion modes and smartphone carrying postures,effectively suppresses false and missed detections, and reduces the PDR positioning errors introduced by the gait detection stage.

Key words: pedestrian dead reckoning(PDR), Mamba neural network, indoor pedestrian positioning, adaptive threshold, gait detection