粒子滤波和多站TOA的外辐射源雷达跟踪方法

针对外辐射源雷达跟踪常采用扩展卡尔曼滤波（extended Kalman filter，EKF）算法导致其跟踪精度受闪烁噪声影响较大的问题，结合到达时间（time of arrival，TOA）定位技术和粒子滤波（particle filter，PF）算法，提出一种适于闪烁噪声环境的外辐射源雷达跟踪方法。该方法通过多站TOA获得测量信息，利用双基地角来减弱目标雷达散射截面积（radar cross section，RCS）闪烁，采用非线性和非高斯的PF进行跟踪，能减小因闪烁噪声而导致的跟踪误差，避免EKF算法因线性化而带来的误差，从而提高跟踪精度。实验表明,该方法的跟踪性能优于EKF，尤其受闪烁噪声影响小，能提高闪烁噪声环境下的跟踪精度。实测数据验证了该方法的有效性。

FM based passive radar tracking using particle filter and TOA measurements

In FM based passive radar, the tracking performance of the traditional extended Kalman filter (EKF) is affected seriously by the glint noise. To solve this problem, a new passive radar tracking method is proposed based on particle filter (PF) and time of arrival (TOA) measurements. The method gains TOA measurements by multi stations, utilizes the different bistatic angles to relieve the radar cross-section (RCS) glint and then uses nonlinear and non-Gaussian PF to track target, which reduces the measurements error resulted from glint noise, avoids the error caused by EKF linearization, so it can improve the passive tracking precision. The simulations show that the performance of the new method is superior to that of the EKF, especially in glint noise environment. The real data demonstrate the validity of the proposed method.