基于自适应卡尔曼滤波的机动目标跟踪算法

摘要

In the process of maneuvering target tracking, because of the uncertainty of moving target, it is difficult to obtain high tracking accuracy with the fixed states model. Therefore, this paper introduced an algorithm based on adaptive Kalman filter combined with Cartesian coordinate system and spherical coordinate system. The algorithm avoids the change of noise statistical regularity resulted from coordinate systems transformation, and to deal with the highly maneuvering target tracking situation, we applied adaptive Kalman filter to adjust adaptively parameters of the state equation. The Monte - Carlo simulation results show that, compared with Kalman filtering algorithm with single coordinate, this algorithm has improved the estimation accuracy of state and convergence rate, and promoted the tracking performance for maneuvering targets.%在机动目标跟踪过程中,由于目标运动的不确定性,雷达系统接收的数据存在噪声,使预置目标运动模型通常很难得到较高的跟踪精度.为此,以自适应卡尔曼滤波为基础,将直角坐标系和球坐标系相结合,提出了一种混合坐标系下的自适应卡尔曼滤波算法.算法避免了两个坐标系变换引起的噪声统计规律变化问题,并针对目标发生大机动运动的情况,自适应的调整动态模型中机动目标运动参数.蒙特卡洛仿真结果表明,改进算法的收敛速度和对状态的估计精度均得到优化,并对机动目标具有较好的跟踪性能.