Bistatic radar target tracking is challenging due to the fact that the measurements are nonlinear functions of the Cartesian state. The converted measurement Kalman filter (CMKF) converts the raw measurement into Cartesian coordinates prior to tracking and is superior to the extended Kalman filter for certain problems. The challenges of CMKF are debiasing the converted measurement and approximating the converted measurement error covariance. Due to no closed form of biases, we utilize the second-order Taylor series expansion of the conventional measurement conversion to find the conversion bias in bistatic radar and propose the unbiased converted measurement (UCM). In order to decorrelate the converted measurement error covariance from the measurement noise, we evaluate the covariance using the prediction in Bayesian recursive filtering, designated as the decorrelated unbiased converted measurement (DUCM). Monte Carlo simulations show that the DUCM is unbiased and consistent, and the DUCM filter exhibits an improved performance compared with the conventional CMKF and the UCM filter in bistatic radar tracking.

中文翻译：

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与装饰相关的无偏转换测量，用于双基地雷达跟踪

由于测量是笛卡尔状态的非线性函数，因此双基地雷达目标跟踪具有挑战性。转换后的测量卡尔曼滤波器（CMKF）在跟踪之前将原始测量值转换为笛卡尔坐标，并且在某些问题上优于扩展的卡尔曼滤波器。CMKF面临的挑战是使转换后的测量值失衡并近似转换后的测量误差协方差。由于没有闭合的偏置形式，我们利用常规测量转换的二阶泰勒级数展开来找到双基地雷达中的转换偏置，并提出无偏置转换测量（UCM）。为了从测量噪声中解相关转换后的测量误差协方差，我们使用贝叶斯递归滤波中的预测来评估协方差，指定为去相关的无偏转换测量（DUCM）。蒙特卡洛模拟显示DUCM无偏且一致，并且在双基地雷达跟踪中，DUCM滤波器与常规CMKF和UCM滤波器相比，具有更高的性能。