Debiased Linear Measurement Matrix-Based Linear Sequential Filtering for Radar Tracking

For radar target tracking with nonlinear position and range rate measurements, a debiased measurement matrix-based linear sequential filtering method with an adaptive parameter is proposed. A concise linear measurement equation with an adaptive parameter is constructed based on the relationship between the pseudomeasurement and target's state vector. For the involved unknown parameters in the measurement matrix, the filtering results from the best linear unbiased estimator are utilized to estimate in the sequential linear filter, where the measurement matrix estimation error is fully considered and is incorporated with the original converted pseudomeasurement error to form a synthetical measurement error. The statistical characteristics of it are deduced based on which the measurement matrix is debiased. The adaptive parameter in the linear measurement matrix is optimized to maximize the information gain in the sequential filtering. Simulation results demonstrate the effectiveness of the proposed algorithm and its extension to the maneuvering target tracking case. Compared with existing algorithms, the proposed ones can achieve the best tracking performance in different scenarios.