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粒子滤波(particle filter,PF)是利用蒙特卡洛仿真方法处理递推估计问题的非线性滤波算法,这种方法不受模型线性和高斯假设的约束,是处理非线性非高斯动态系统状态估计的有效算法,适用于雷达回波反演大气波导(RFC)这类非线性非高斯问题.文中分别介绍了PF的基本思想和具体算法实现步骤,最后导出PF反演算法的迭代求解格式.数值试验结果表明,与扩展卡尔曼滤波(extended kalman filter,EKF)和不敏卡尔曼滤波(unscented kalman filter,UKF)相比,PF更适用于RFC这类高度非线性反演问题,可有效提高反演结果的稳定性和精度.Particle filter(PF) is an effective algorithm for the state recursive estimation in nonlinear and non-Gaussian dynamic systems by utilizing the Monte Carlo simulation, and it is applicable for solving the nonlinear and non-Gaussian RFC(refractivity from radar clutter) problems. The basic idea and the specific algorithm of PF are introduced; the implementation of the iterative inversion algorithm is derived finally. The experimental result indicates that the particle filter is suited to solve the nonlinear inversion problem and can effectively increase the stability and the accuracy of inversion results compared with the extended Kalman filter (EKF) and the unscented kalman filter (UKF).
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Keywords:
- atmospheric ducts/
- radar clutter/
- particle filter (PF)
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