Foot-Mounted Inertial Navigation Algorithm Based on Dynamic Threshold Gait Detection and Adaptive Step Length Estimation

Abstract In indoor environments where satellite signals are obstructed, inertial navigation technology demonstrates a high level of autonomy and interference resistance. Compared to other pedestrian inertial positioning systems, foot-mounted inertial navigation systems exhibit distinct gait characteristics and periods of stasis, offering favorable conditions for error correction in low-cost micro-electro-mechanical system (MEMS) inertial positioning systems, which suggests a broad range of applications. However, current gait detection thresholds are static, the determination methods are singular, interval division is ambiguous, and there are significant issues with missed and false detections. Moreover, inertial navigation algorithms lack positional information observation, and without external information assistance, the positioning results tend to diverge over time. Existing step length models that constrain position have fixed parameters, leading to poor adaptability. To address these challenges, this paper introduces a foot-mounted inertial navigation algorithm that is based on dynamic threshold gait detection and adaptive step length estimation. Initially, a dynamic threshold gait detection method is proposed, which integrates multi-condition detection based on the transformation rules of foot motion information. Subsequently, leveraging the high short-term precision of inertial navigation algorithms, the linear step length estimation model is refined to establish an adaptive step length estimation model with self-regulating parameters. Ultimately, the estimated single-step displacement of the foot is utilized to estimate positional information as an observation, thereby correcting the positional errors of the foot-mounted system. In multiple 1000 m open-loop tests, the algorithm presented in this paper achieved a maximum endpoint error of 12.54 m, a mean error of 11.68 m, and a root mean square error (RMSE) of 11.70 m.