Complex Motion Modeling and State Estimation in Road Coordinates

Abstract

Constrained modeling and state estimation have attracted much attention in recent years. This paper focuses on target motion modeling and tracking in road coordinates. An improved initialization method, which uses the optimal fusion of the position measurements in different directions, is presented for the constraint coordinate Kalman filter (CCKF). The CCKF is evaluated with a comprehensive comparison to the state-of-art linear equality constraint estimation methods. Numerical simulation results demonstrate the better performance of the CCKF. Then the interacting multiple model CCKF (IMM-CCKF) is proposed to manifest the advantages of the CCKF in complex motion modeling and state estimations. The effectiveness of the IMM-CCKF in maneuvering target tracking with spatial equality constraints is demonstrated by numerical experiments.