Model-switched Gaussian sum cubature Kalman filter for attitude angle-aided three-dimensional target tracking

摘要

Only target kinematic information is used in most conventional tracking systems, such as a radar or a sonar. Target attitude angles, which provide information about future trajectory curvature before radar measurement, can be used to improve tracking accuracy. The aim of this study is to track three-dimensional (3D) target with attitude angles (yaw and pitch) and radar measurement. Target velocity variations in each coordinate under attitude angles are derived after motion analysis under yaw and pitch angles separately. Then tracking models, the state vector of which includes attitude angles, are presented for target tracking. Targeting at non-zero mean characteristics of attitude measurement, and based on analysing Gaussian sum filter (GSF) and cubature Kalman filter (CKF), a Gaussian sum CKF (GSCKF) is presented to improve the filtering ability of non-linear non-Gaussian systems. Meanwhile, tracking models with different attitude components are established according to the changing law during target motion, and manoeuvering attitude angles are estimated through model switch. A comparison of performance with and without the use of attitude angles shows the benefits of attitude angle-aided 3D target tracking. Simulation results of GSFs with different sub-filters demonstrate that the performance of the presented GSCKF has improved over conventional GSFs.