An Evidence-Weighted Multi-Rate Inertial Fusion Method for Industrial Load Displacement Reconstruction

An evidence-weighted multi-rate inertial fusion method is proposed for load displacement reconstruction in high-vibration industrial transportation scenarios, where inertial displacement reconstruction is readily affected by acceleration bias, attitude error, vibration disturbance, and false zero-velocity decisions. Multi-rate inertial data and high-bandwidth vibration/impact observations are used. The main inertial channel is used for motion-dynamics modeling, low-frequency inertial and attitude information is used to constrain the trajectory trend, and high-bandwidth vibration and impact responses are converted into motion-veto evidence so that unreliable stationary decisions under high-vibration conditions can be weakened. A Dempster–Shafer-style evidence fusion model is adopted to estimate motion, stationary, and unknown confidence, and the stationary confidence is propagated to ZUPT gating, covariance weighting, multi-branch displacement fusion, and keyframe factor-graph optimization. The experimental results show that stable load displacement reconstruction can be achieved by the proposed method, while interpretable diagnostic information, including displacement, velocity, stationary probability, ZUPT triggering, residuals, and drift risk, is also output. A diagnosable and auditable framework for inertial displacement reconstruction is therefore provided for high-vibration industrial scenarios where external references are difficult to deploy.