地面动目标运动速度较小,且运动模型随着道路实时变化,在远距离前提下机载雷达如何对地面动目标进行精确跟踪已成为亟待解决的难题。针对该问题,文中提出了一种自适应转弯无迹滤波算法。首先,通过推导目标位置、速度与转弯率的关系,并将其作为状态变量进行自适应更新,提高了跟踪模型准确性;其次,引入无迹变换的思想,有效减小非线性估计误差。仿真实验表明,所提算法的位置和速度均方根误差均小于传统的Singer和交互式多模型算法,为地面动目标精确跟踪提供了一种新的思路。

The velocity of ground moving target is rather small and its dynamic model is varying with the road net. Hence, how to precisely track the ground moving target from long range using airborne radar has become an urgent problem. To solve this problem, an adaptive turn unscented Kalman filter (ATUKF) is proposed, which first derives the relationship between position and velocity with the turn rate, and updates the turn rate adaptively. Besides, the unscented transform (UT) is introduced to reduce the nonlinear estimate error. Simulation results show that the proposed algorithm reduces the tracking position and velocity root mean square (RMS) errors comparing with Singer and interacting multiple models(IMM), which provides a new precise tracking method for ground moving target.

国家自然科学基金资助项目