基于UKF的单站无源定位与跟踪双向预测滤波算法

提出了一种新的滤波算法，以加快滤波算法的收敛速度和提高滤波的估计精度。反向预测与更新提高了上一时刻状态估计的精度，减小了当前时刻的状态预测误差。利用更准确的初始条件经过正向预测与更新，能得到当前状态更精确的估计值。计算机仿真结果表明，本算法的滤波性能优于传统的迭代滤波算法，既提高了滤波的估计精度，又加快了算法的收敛速度。

Unscented Kalman filter with forward-backward prediction for single observer passive location and tracking

A novel unscented Kalman filter (UKF) algorithm is proposed to improve convergence speed and estimation accuracy. The backward prediction and the state update procedure improve the estimation accuracy of the last state estimate and reduce the current state prediction error. A more accurate current state estimate could be gotten with more precise initial condition. Simulation results show that the performance of the proposed algorithm outperforms that of the conventional iterated UKF in both convergence speed and estimation accuracy.