论文标题
反对双偏的转换测量的双雷达跟踪
Decorrelated Unbiased Converted Measurement for Bistatic Radar Tracking
论文作者
论文摘要
通过Bistatic雷达测量值的跟踪由于测量值是笛卡尔状态的非线性函数，因此具有挑战性。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，从而避免了扩展的卡尔曼滤波器（EKF）的陷阱。 CMKF的挑战在于转换的测量值并近似转换后的测量误差协方差。由于没有偏见的封闭形式，该字母利用了常规测量转换的二阶泰勒串联序列扩展，以找到双基于雷达中的转换偏置，该转换偏置得出了无偏转换的测量（UCM）。为了使转换的测量误差协方差从测量噪声降低，该预测被用来评估协方差，该协方差得出了反相关的无偏置转换测量（DUCM）的协方差。蒙特卡洛模拟表明，与传统的CMKF和Bistatic雷达跟踪中的常规CMKF和UCM滤波器相比，DUCM滤波器表现出改善的性能。
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.
