Maneuvering target tracking in constraint coordinates with radar measurements

Maneuvering target tracking in constraint coordinates have attracted much research attention in recent years. This paper follows the one-dimension (1D) motion modeling method and focuses on target tracking in constrained road coordinates. An improved initialization method which combines the measurements in different dimensions is presented for the constraint coordinates Kalman filter (CCKF). The CCKF is evaluated with a comprehensive comparison to the state-of-art linear equality constraint estimation methods. Original radar measurements are transformed from polar coordinate to Cartesian coordinate by debiased converted measurement (DCM) method. Numerical simulation results demonstrate the better performance of the CCKF. The filtering consistency of the CCKF is proved by normalized estimation error squared (NEES) test. Then, the interacting multiple model CCKF (IMM-CCKF) is proposed to illustrate the advantages of the CCKF in maneuvering target tracking with spatial equality constraints. The effectiveness of the IMM-CCKF is demonstrated by numerical experiments.