可处理多普勒量测的最佳线性无偏估计算法

基于目标位置量测的一些量测转换方法已被广泛使用在目标跟踪应用中，使得卡尔曼滤波器得以在直角坐标系中应用。但是，这些量测转换方法有一些会导致估计性能恶化的根本缺陷。事实上，除了位置量测外，理论计算和实践已经证明，包含目标速度信息的多普勒量测具有有效提高目标状态估计精度的潜力。该文在直角坐标系下提出一种可使用转换多普勒量测(即距离量测与多普勒量测的乘积)的滤波器。从理论上讲，它是在最佳线性无偏估计准则下的最优线性无偏滤波器，并且避免了量测转换方法的根本缺陷。通过将近似处理后的新型最优线性滤波器与目前4种流行的方法进行仿真比较，验证了所提出的滤波器的优越性。

The Best Linear Unbiased Estimation Algorithm with Doppler Measurements

A number of measurement-conversion techniques, which are based on position measurements, are widely used in tracking applications, so that the Kalman filter can be applied to the Cartesian coordinates. However, they have fundamental limitations resulting in filtering performance degradation. In fact, in addition to position measurements, the Doppler measurement containing information of target velocity has the potential capability of improving the tracking performance. A filter is proposed which can use converted Doppler measurements (i.e. the product of the range measurements and Doppler measurements) in the Cartesian coordinates. The novel filter is theoretically optimal in the rule of the best linear unbiased estimation among all linear unbiased filters in the Cartesian coordinates, and it is free from the fundamental limitations of the measurement-conversion approach. Based on simulation experiments, an approximate, recursive implementation of the novel filter is compared with those obtained by four state-of-the-art conversion techniques recently. Simulation results demonstrate the effectiveness of the proposed filter.