均方意义下的最优无偏转换测量Kalman滤波

转换测量Kahmal滤波(CMKF)在雷达跟踪领域得到了广泛的应用。然而，当目标位置的互距离测量误差比较大时，CMKF的性能将急剧下降。本文在均方意义下给出了一种新的无偏CMKF。这种滤波器的关键是在均方意义下推导无偏转换测量误差协方差阵的最佳估计。对于匀速运动目标，仿真结果表明本文方法可以得到好的滤波性能，而当方位测量误差比较大时，滤波性能的改善就更加明显。此外，对机动目标的情形亦进行了讨论。

Optimal Unbiased Converted Measurement Kalman Filtering in the Mean-Square Sense

The converted measurement Kalman filter (CMKF) has been widely used for radar tracking problem. However, it has been found that the performance of CMKF degrades considerably when cross-range measurement errors of the target position are large. Due to the advantage of exact debiasing of unbiased CMKF, a new Unbiased CMKF (UCMKF) is presented. The key of the presented filter is to derive the best estimate of the covariance matrix of the unbiased converted measurements in the mean-square sense. For constant-velocity target, simulation is made and it is seen from the simulation results that the presented method can get better performance especially when the standard deviation of azimuth qs increases. In addition, discussion is also made for maneuvering target case.