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应用科学学报 >

2026 , Vol. 44 >Issue 4: 669 - 684

DOI: https://doi.org/10.3969/j.issn.0255-8297.2026.04.011

智能视觉感知

基于Mamba模式感知的步态检测算法及其在PDR中的应用

  • 周子毅 ,
  • 刘德儿 ,
  • 钟崇林 ,
  • 王源 ,
  • 张涛 ,
  • 邱永康
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  • 江西理工大学 低空技术学院, 江西 赣州 341000

收稿日期: 2026-01-19

  网络出版日期: 2026-08-01

基金资助

地球深部探测与矿产资源勘查国家科技重大专项(No.2025ZD1011200);国家自然科学基金(No.42271434)

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Mamba-Based Pattern-Aware Gait Detection Algorithm and Application in Pedestrian Dead Reckoning

  • ZHOU Ziyi ,
  • LIU Deer ,
  • ZHONG Chonglin ,
  • WANG Yuan ,
  • ZHANG Tao ,
  • QIU Yongkang
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  • School of AirSpace Technology, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, China

Received date: 2026-01-19

  Online published: 2026-08-01

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摘要

针对步态检测算法在复杂运动模式与多变设备携带姿态条件下易产生漏检与误检的问题，本文提出一种融合Mamba神经网络与有限状态机（finite state machine,FSM）的自适应步态检测算法。通过Mamba网络构建分类模型实现对行人运动模式及手机携带姿态的联合判别，并融合自适应阈值FSM、角速度回溯机制以及基于重力投影的非刚性耦合去噪策略，实现复杂携带条件下步态事件的稳定触发，进一步提升行人航位推算（pedestrian dead reckoning,PDR）在复杂场景下的定位精度与鲁棒性。实验结果表明，所提算法在多种运动模式与手机携带姿态条件下均表现出较高的步态检测准确性，有效抑制了误检与漏检现象，降低了由步态检测环节引入的PDR定位误差。

关键词： 行人航位推算; Mamba神经网络; 室内行人定位; 自适应阈值; 步态检测

本文引用格式

周子毅 , 刘德儿 , 钟崇林 , 王源 , 张涛 , 邱永康 . 基于Mamba模式感知的步态检测算法及其在PDR中的应用[J]. 应用科学学报, 2026 , 44(4) : 669 -684 . DOI: 10.3969/j.issn.0255-8297.2026.04.011

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

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