Motion Matching Factor-Informed Foot Kinematics/INS Fusion for Pedestrian Autonomous Positioning Under Complex Gaits

Addressing the intermittent failure of traditional zero-velocity update (ZUPT)/inertial navigation system (INS)-based pedestrian positioning under complex gaits, this article proposes an integrated solution - motion matching factor (MMF)-informed foot kinematics/INS fusion method. MMF, embedded within the Kalman filtering framework, is introduced to quantify the alignment between the kinematics model and reality. Leveraging inherent properties of the filter, the innovation nonorthogonality factor (INOF) and residual factor (RF) are constructed as MMF. Subsequently, an online clustering method is proposed to evaluate the matching level based on MMF in real time and adaptively determine the necessity of fusing kinematics information. Instead of focusing on optimizing the fusion detector, this method adopts a simple quasi-zero-velocity detector with a large threshold based on angular velocity modulus. The challenge of identifying false fusion moments is reframed as a fusion decision-making problem grounded in evaluating the matching level between kinematics model and reality. Experimental results demonstrate that under mixed motion states - including slow walking, fast walking, and running - the proposed algorithm maintains high positioning accuracy and computational efficiency while also exhibiting simplicity, ease of implementation, and strong adaptability.