WK混合滤波算法在雷达数据处理中的应用

通常的Kalman滤波算法不能很好的解决信号的时频局部性问题,往往只能在低频和高频两种滤波算法间通过机动检测器(变维滤波算法)或马尔可夫链的转移概率矩阵(VD算法)来进行切换,因为有一定的滞后,并受到机动检测器和转移概率矩阵的影响较大,从而产生较大的滤波误差.笔者将二维可分离小波变换良好的时频局部性和Kalman滤波的实时最佳预测修正跟踪滤波估计结合起来,得出一种有效的混合滤波算法(WK算法),并将该算法用于进行雷达数据的滤波处理,使得滤波估计值逐步逼近真实轨迹.通过实际的仿真验证了该算法比其它单一的滤波算法更为有效.

Application of the WK Mixed Filter Algorithms in the Processing of Radar Information

The Kalman filter algorithms can not solve the problem of the time-frequency localization, and it often switches between the low-frequency filter and high-frequency filter by the mobile detector (The changeable dimension filter algorithms) or Markovian transfer probability matrix (VD algorithms), so it has delay and some influence of the the mobile detector and Markovian transfer probability matrix. Hence, the filter error of the Kalman filter algorithms is obviously big. This paper brings the new mixed filter algorithms (WK algorithms) of the 2D Wavelet transform and Kalman Filter, which has the characteristics of the well time-frequency localization and real-time. The WK algorithms is used to process the radar information and makes the filter estimated data to approach the true track.