扩展卡尔曼滤波和不敏卡尔曼滤波在实时雷达回波反演大气波导中的应用

Tracking refractivity from radar clutter using extended Kalman filter and unscented Kalman filter

为了改善雷达回波反演大气波导(RFC)方面存在的单时次、单方位角反演的问题,提出利用扩展卡尔曼滤波和不敏卡尔曼滤波的反演算法对大气波导结构的多方位角实时跟踪反演.在卡尔曼滤波方法中分别给出大气波导结构的参数化方程、观测方程、滤波算法的状态转移方程,最后导出滤波反演算法的迭代求解流程.在大气波导结构不随时间变化和随时间变化的两种条件下,对扩展卡尔曼滤波和不敏卡尔曼滤波算法进行数值实验.实验结果表明,不敏卡尔曼滤波更适用于RFC这高度非线性反演问题,它可能今后为大气波导结构多方位角实时跟踪反演的业务化运行提供理论基础与技术保证.

Since the traditional statistical and physical algorithms in the inversion of refractivity from radar clutter （RFC） cannot track omni directions in real time,a new filter algorithm （extended Kalman filter and unscented Kalman filter） is proposed.The parameter equation of atmospheric duct,observation operator,state equation of filter arithmetic are derived separately.Finally,the implementation of the iterative inversion filter algorithm is derived.On the theoretical basis,two algorithms above are tested separately with or without considering the variation of refractivity with time.The experimental result indicates that unscented Kalman filter is suited to solve the nonlinear inversion problem,which has significance in theoretical foundation and technological support for practical applications for the future.