基于距离参数化的混合坐标系下平方根容积卡尔曼滤波纯方位目标跟踪

针对纯方位单站目标跟踪中观测方程非线性且易受滤波初值影响的问题,提出了一种距离参数化混合坐标系下的平方根容积卡尔曼滤波(SRCKF)算法.该滤波算法首先将平方根容积卡尔曼滤波算法应用于混合坐标系,比直角坐标系下的平方根容积卡尔曼滤波算法能得到更好的跟踪效果;接着将距离参数化思想和混合坐标系下的平方根容积卡尔曼滤波算法结合,消除了距离信息不可测对跟踪效果的影响.仿真结果表明,该滤波算法虽略微提升了计算复杂度,但其鲁棒性和滤波精度均有大幅度的提高.

Range-parameterized square root cubature Kalman filter using hybrid coordinates for bearings-only target tracking

In order to solve the problems of having nonlinear observation equations and being susceptible to initial value of filtering in bearings-only target tracking, a range-parameterized hybrid coordinates Square Root Cubature Kalman Filter (SRCKF) algorithm was proposed. Firstly,it applied the SRCKF to hybrid coordinates,obtained better tracking effect than the SRCKF under Cartesian coordinates. And then it combined the range parameterization strategy with the SRCKF under hybrid coordinates, and eliminated the impact of unobservable range. The simulation results show that the proposed algorithm can significantly improve the accuracy and robustness although the computational complexity increases slightly.