粒子滤波在误差配准中的应用

Using Partide Filtration for Registration

系统偏差严重影响了多部雷达航迹的互联和目标状态的融合,因此如何解决误差配准成为一个关键问题。因为系统偏差和雷达的量测之间是一种非线性关系,所以无法直接使用Kal-man等线性滤波直接进行系统偏差的估计。为解决这一问题,利用非线性的粒子滤波器(PF),对目标状态和系统偏差的估计进行了探讨,为了对比其性能,设计了一种扩展Kalman滤波算法。仿真结果表明PF在误差配准中的应用是可行的,性能高于扩展Kalman滤波。

Radar registration becomes a key problem because of its heavy effect on radar data fusion. The extended Kalman filter （EKF） is generally used to linearize the state or measure equations of the nonlinear system, and the linear method can be used. Because of the hard nonlinearity between alignment and radar measure, however,the performance of the EKF may not be good due to the linearization error. In this paper a new method is proposed. Firstly, the method transforms the measure equation of the system, so the colore noise of the equation can be changed into white noise. Then, particle filter （PF） is used to estimate the state of the system. The results of the simulation show that the new method is useful and effective, compared with EKF,it can effectively get the more precision state estimation of the nonlinear system.