A Robust Converted Measurement Kalman Filter for Target Tracking

This paper proposes a robust converted measurement Kalman filter (CMKF) algorithm to realize the target tracking with nonlinear measurement equations. At each processing index, the new algorithm chooses the more accurate state estimate from the state prediction and the sensor's measurement. The new algorithm then computes the converted measurement's error mean and the corresponding debiased converted measurement's error covariance conditioned on the chosen state estimate. Simulation results demonstrate the new CMKF's robust tracking performance as compared to the traditional DCMKF and MUCMKF. As a conclusion, the proposed algorithm can realize the target tracking with the non-linear measurement equations with well performance in different scenarios.