基于无味卡尔曼滤波的多雷达方位配准算法

将无味卡尔曼滤波（Unscented Kalman filter，UKF）应用于雷达配准，提出一种新的多雷达方位配准算法。在该算法中，目标的运动状态和方位误差由选定的采样点来近似，在每个更新过程中，采样点随着状态方程传播并随非线性测量方程变换，得到目标的运动状态和方位误差的均值，避免了对非线性方程的线性化，且具有较高的计算精度。与传统的扩展卡尔曼滤波（Extended Kalman filter，EKF）方法进行了仿真比较，结果表明UKF方法能有效地克服非线性跟踪问题中很容易出现的滤波发散问题，且估计精度高于UKF方法。

Algorithm for Multi-Radar Azimuth Registration Based on Unscented Kalman Filter

The unscented Kalman filter(UKF) is applied to the radar registration and a new algorithm for the multi-radar azimuth registration is presented.In the algorithm,the target state and the biased error estimates are approximated by specified sample points.During the each update process,sample points are propagated by the state equation and transformed by the nonlinear measurement equation.From these sample points,the posterior mean and covariance of the target state,and the biased error are accurately computed to the second order.The linearization of the nonlinear equations for the extended Kalman filter(EKF)is not needed.Simulation result shows that in the radar registration problem the UKF method outperforms the standard EKF in the accuracy and the divergence performance.