基于平方根UKF的多传感器融合再入段目标跟踪研究

Multi-sensor fusion target tracking of reentry phase based on square-root unscented Kalman filter

为了提高再入段目标跟踪的精度,将平方根不敏卡尔曼滤波(unscented Kalman filter,UKF)算法与多传感器分布式融合算法相结合,提出了基于平方根UKF的多传感器融合跟踪算法。在各个独立的传感器中利用平方根UKF滤波器进行状态估计,然后通过分布式融合方法融合各传感器的状态估计值得到全局的状态估计值和误差协方差,将全局误差协方差进行加权对各传感器进行分配更新。通过仿真验证,基于平方根UKF的多传感器融合跟踪算法具有较高的跟踪性能,是一种有效的非线性融合跟踪算法。

In order to improve the tracking accuracy of targets in reentry phase, a new distributed fusion algorithm is proposed by combining the square-root unscented Kalman filter （UKF） with the multi-sensor distributed fusion tracking algorithm. This algorithm uses the square-root UKF to calculate the state estimation values of local sensors respectively, and consequently the system fusion state estimation and covariance is obtained by applying the multi-sensor distributed fusion rules. At the same time, the state covariance is allocated to the local sensors. The simulation results show that the new algorithm has a higher tracking performance, and it is a very effective nonlinear fusion tracking algorithm.