Adaptive Mid-Stance Phase Observer-Aided Pedestrian Inertial Navigation System for Varying Gait Speeds

Accurate and self-contained pedestrian inertial navigation systems (PINSs) are urgently needed for military and civilian wearable Internet of Things applications and location-based services. The zero-velocity update-based PINS faces two challenges of incorrect zero-velocity detection and invalid zero-velocity assumption when the pedestrian randomly changes gait speed and gait cadence, which lead to rapid divergence of position errors and serious underestimation of mileage, respectively. Therefore, an adaptive mid-stance observer (AMSO) is proposed to overcome the challenges above, which consists of a mid-stance phase detector (MD) and a lever-arm velocity observation (LAVO). First, MD establishes a magnetic field that is periodically distorted with the gait cycle to constrain the generalized likelihood ratio detector to reliably detect the mid-stance phase. Second, LAVO infers the pseudo velocity by compensating the lever-arm effect error during mid-stance phase, and its noise is modeled as a first-order Markov process that attenuates with the increase of the stance stability. To eliminate the magnetic heading error caused by distorted magnetic field, a dynamic time warping method is then used to determine the effective intervals of heading observation and correct the heading of inertial navigation. Finally, the colored-noise Kalman filter is used to fuse all observations and correct the positioning results of PINS. The experimental results showed that the proposed method can effectively improve the positioning accuracy despite the gait speed was switched randomly. The strong adaptability and robustness meet the needs of large-scale pedestrian navigation applications.