组网雷达的改进粒子滤波算法

针对现代战争中有源雷达容易受到干扰和反辐射导弹的摧毁,以及无源雷达隐蔽性高,只能测量方位角度,测量精度小等特点,提出利用集中式有源雷达系统与无源雷达系统协同组网对目标进行跟踪。但是在实际环境中,噪声属性以及有源雷达,无源雷达接收信号的特点决定了组网雷达需要应用非线性滤波技术对信号进行处理。传统的非线性技术包括卡尔曼滤波、扩展卡尔曼滤波(EKF)或无迹卡尔曼滤波(UKF)等方法。非线性近似过程带来的误差相对较大,而且均要求观测噪声和过程噪声为独立或相关的高斯白噪声。而粒子滤波避免了传统非线性滤波方法的缺陷,但是存在粒子退化,于是用EKF和UKF在每一时刻更新粒子,用更新的粒子及其协方差构造重要性函数,然后重采样。仿真实验表明这两种改进粒子滤波方法有很好的效果。

An Approach Based on Networked Radars for Improved Particle Filter Algorithm

In this paper,a new target tracking method using the centralized active radars system cooperated with passive radars system is proposed,based on the active radars easily being subjected to jamming and attacked by anti-radiation missile in modern war,and the good performance of passive radars being concealment,the azimuth angle only being measured and the bad precision of the measure.But in the fact,because of the noise attribute and the characteristics of signal which received by passive radars,the signal should be processed using non-linear filters in the networked radars system.The conventional nonlinear filters include Kalman Filter(KF),Extended Kalman Filter(EKF),Unscented Kalman Filter (UKF) and so on.The non-linear approximate process brings much error,and the observation noise and the process noise should be independence and correlation or Gaussion white noise.Although the Particle Filter can avoid the shortage of traditional non-linear filters,it has particles degradation.Thus,the method which combines EKF with UKF to update the particles at every time,and uses updated particles and their covariance constructing importance function,then re-sampling.The results of the simulation show that the new algorithm has the better effect.