一种基于径向加速度的Singer-EKF机动目标跟踪算法

Tracking Algorithm of Singer-EKF Maneuvering Target Based on Radial Acceleration

摘要:

针对雷达均不能提供目标加速度信息,在目标机动时会出现跟踪精度差甚至跟踪发散的问题,提出一种基于径向加速度的Singer-EKF算法。该算法在信号处理阶段利用Radon-Ambiguity变换(RAT)估计出目标的径向加速度,并通过坐标转换将其引入量测向量中,然后采用基于Singer模型的扩展卡尔曼滤波(EKF)算法实现机动目标的跟踪。仿真验证了该方法的有效性,并与传统的不带径向加速度的扩展卡尔曼滤波(EKF)方法进行了比较,结果表明该方法在径向距离、位置、加速度和速度估计精度方面都有所提高。

Abstract:

The performance of maneuvering tracking can be improved if acceleration is derived. Therefore, a Singer-EKF method based on Radon-Ambiguity transformation (RAT) was proposed for enhancing the tracking of a maneuvering target. In the proposed method, the radial acceleration was derived based on Radon-Ambiguity transformation in signal processing and then brought into the measurement vector coordinates transform model. In the filtering approach, a method of Extended Kalman Filter (EKF) based on Singer model was adopted to resolve the problem of the non-linearity of the measurement equation. The tracking performance of the proposed method is evaluated and compared with the traditional Extended Kalman Filter (EKF) algorithm based on the Singer model without acceleration measurement. It is showned that the proposed algorithm outperformed better in maneuvering scenario, and the estimate accuracies of radial distance, position, acceleration and velocity were improved at the same time.