基于高斯和均方根容积卡尔曼滤波的姿态角辅助目标跟踪算法

Pose Angle Aided Target Tracking Algorithm Based on Gaussian Sum Square-root Cubature Kalman Filter

根据目标2维运动速度与姿态角的关系,该文提出一种姿态角辅助目标跟踪算法。在目标运动学基础上建立状态向量中包含姿态角的跟踪模型,实现姿态角对目标跟踪的辅助;针对基于模板匹配姿态角量测的噪声为非高斯情况,将均方根容积卡尔曼滤波引入到高斯和滤波框架下,提出新的高斯和均方根容积卡尔曼滤波算法,提高非线性非高斯处理能力,同时结合目标运动中姿态角的变化规律,建立姿态角分量不同的跟踪模型,通过模型切换实现机动姿态角的滤波。算法对姿态角量测进行滤波,同时实现了姿态角信息与位置信息的有效融合。仿真结果验证了该算法的有效性和正确性。

Based on the relationship between the velocities of 2D motion and the pose angle, a pose angle aided target tracking algorithm is proposed. In terms of target kinematics, the tracking models, in which the state vector includes pose information, are constructed to realize the aiding for target tracking. In order to improve the filtering ability of nonlinear non-Gaussian systems, the Gaussian Sum Square-root Cubature Kalman Filter （GSSCKF） algorithm is proposed by introducing Square-root Cubature Kalman Filter （SCKF） into the framework of Gaussian Sum Filter （GSF）, due to the non-Gaussian pose measurement noise obtained by model matching. Moreover, tracking models with different pose components are established by exploiting the pose variation law in targets motion, and maneuvering pose is estimated by model switching. The proposed algorithm is able not only to filter the pose measurement, but also to fuse the pose information and the position information effectively. The simulation results show the validity and the correctnesss of the proposed algorithm.