一种带径向速度量测的Jerk-EKF机动目标跟踪算法

为了提高机动目标跟踪精度,在基于Jerk模型的扩展卡尔曼滤波算法(Jerk-EKF)基础上,提出了一种带径向速度量测的扩展卡尔曼滤波算法(Jerk-EKFrv).该算法通过引入径向速度量测扩充了量测矩阵的维数,然后利用展开泰勒级数的一次项,解决量测方程中状态向量和量测向量的非线性问题,最后采用卡尔曼滤波算法对目标状态进行估计.对Jerk-EKF和Jerk-EKFrv算法的仿真结果表明,Jerk-EKFrv算法能够有效提高机动目标的跟踪精度.

Algorithm for Jerk-EKF Maneuvering Target Tracking With Radial Velocity Measurement

To raise the tracking precision of maneuvering target, this paper puts forward an extended Kalman filtering algorithm with radial velocity measurement （Jerk-EKFrv）, in terms of the extended Kalman filtering algorithm （Jerk-EKF） based on Jerk model. In this algorithm, the dimension of measurement matrix is extended by introducing the radial velocity measurement, then, the non-linearity of the state vector and measurement vector in measurement equations is solved, by using the first term of Taylor series expansion, and finally the target sate estimation can be done by using Kalman filtering algorithm. Simulation results of Jerk-EKF and Jerk-EKFrv algorithms show that the Jerk-EKFrv algorithm could effectively improve the tracking precision of maneuvering target.