双基地雷达跟踪的去相关无偏转换测量
Decorrelated Unbiased Converted Measurement for Bistatic Radar Tracking
摘要：
由于双基地雷达测量是笛卡尔状态的非线性函数，因此跟踪具有挑战性。转换测量卡尔曼滤波器（CMKF）在跟踪之前将原始测量转换成笛卡尔坐标，这避免了扩展卡尔曼滤波器（EKF）的陷阱。CMKF的挑战是去偏转换的测量和近似转换的测量误差协方差。由于双基地雷达中没有封闭形式的转换偏差，本文利用常规测量转换的二阶泰勒级数展开，求出了双基地雷达中的转换偏差，得到了无偏转换测量（Unbiased Converted Measurement，UCM）。为了将转换后的测量误差协方差与测量噪声解相关，利用预测来估计协方差，从而导出解相关无偏转换测量（DUCM）。蒙特卡洛仿真结果表明，DUCM滤波器具有良好的一致性和无偏性，与传统的CMKF和UCM滤波器相比，DUCM滤波器在双基地雷达跟踪中具有更好的性能。
英文摘要：
Tracking with bistatic radar measurements 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, which avoids the pitfalls of the extended Kalman filter (EKF). The challenges of CMKF are debiasing the converted measurement and approximating the converted measurement error covariance. Due to no closed form of biases, this letter utilizes the second order Taylor series expansion of the conventional measurement conversion to find the conversion bias in bistatic radar, which derives the Unbiased Converted Measurement (UCM). In order to decorrelate the converted measurement error covariance from the measurement noise, the prediction is utilized to evaluate the covariance, which derives the Decorrelated Unbiased Converted Measurement (DUCM). Monte Carlo simulations show that the DUCM is unbiased and consistent, and the DUCM filter exhibits the improved performance compared with the conventional CMKF and the UCM filter in bistatic radar tracking.
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