Motion-constrained pedestrian tracking framework based on distributed inertial sensors

Abstract

The pedestrian tracking and motion detection system (P-TMDS) using distributed inertial sensors has broad application potential toward many emerging fields, such as motion tracking, emergency rescue, and others, due to its advanced autonomous navigation capabilities under signal-denied scenarios. The performance of current P-TMDS is constrained by the cumulative error of low-cost sensors, low accuracy of human motion detection, and lack of effective multi-sensor integration algorithms. This paper proposes a motion-constrained P-TMDS based on the adaptive integration of distributed inertial sensors and ultrasonic ranging (MP-TMDS). An enhanced position–attitude update algorithm is developed for the single-sensor module, which integrates the inertial navigation system (INS) mechanization with multi-level constraints and observations. In addition, a bi-directional long short-term memory (Bi-LSTM) structure is adopted to detect the outlier in ultrasonic ranging results and provide accurate distance observations for dual sensor module-based positioning systems. For the overall MP-TMDS, the measurements provided by distributed sensor modules and ultrasonic ranging are adopted as the input vector of designed spatial–temporal network training for human motion detection and walking speed estimation, and the detected human motion modes are further applied as the constraints for multi-module position–attitude update. Finally, an enhanced data and model dual-driven structure is proposed to adaptively integrate motion features acquired from distributed sensor modules and results of velocity and motion detection provided by spatial–temporal network. Real-world experiments in complex scenes represent that the developed MP-TMDS effectively increases the precision of traditional P-TMDS and outperforms existing algorithms under both positioning and motion detection accuracy indexes, and the estimated accuracy improvement is more than 18.4% compared with state-of-the-art algorithms.