Pseudoredundant Measurement-Based Adaptive Filtering for Estimation of Measurement Noise Covariance

Accurate specification of the process-noise covariance Q and measurement-noise covariance R is essential for optimal Kalman filtering. Conventional adaptive algorithms typically estimate R from innovations, thereby coupling the R update with state estimation errors and causing adaptation bias under model mismatch. This article proposes a pseudomeasurement second-order mutual difference (PMSOMD) framework to robustly estimate R in single-measurement systems. The key idea is to construct two pseudomeasurements from state predictions at consecutive time instants and apply a first-order self-differencing (FOSD) operation to the sequence. This effectively suppresses the dominant modeling-error terms, thereby reducing the sensitivity to inaccurate Q and enabling reliable R adaptation even under process-noise mismatch. Simulation results in a maneuvering target-tracking scenario demonstrate that PMSOMD achieves more accurate and stable R estimation compared to the innovation-based adaptive estimation (IAE) and the limited memory-based random-weighted Kalman filter (LMRWKF), particularly when the assumed Q is scaled by factors of 10 100. Real-world experiments further validate the effectiveness of the proposed method.