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中文摘要：
单站无源定位可观测性弱、参数测量精度不高，因此初始值测量误差往往较大。而无迹卡尔曼滤波（Un—scented Kalman filtering，UKF）算法对初始值较为敏感，并且由于数值计算的舍入误差会产生滤波发散，为了解决以上问题，提出了一种基于平方根UKF（Square—root UKF，SRUKF）的双向滤波算法。该算法通过使用误差协方差的平方根替代协方差阵参与滤波，保证了算法的稳定性，同时运用平方根无迹卡尔曼滤波后向平滑（Un—scented Rauch—Tung—Striebel smoother，URTSS）后向平滑方法，用平滑值取代初始值，为前向滤波提供较高精度的起始值，提高算法的滤波精度，从而提高了算法对初始值的鲁棒性。仿真结果表明，与UKF算法和SRUKF算法相比，该算法提高了滤波的稳定性、收敛速度、定位精度及对初始值的鲁棒性。
英文摘要：
The observability and measurement accuracy are low in single observer passive loca- tion, so the initial error is usually large. As the unscented Kalman filtering （UKF） in single observer passive location is sensitive to the initial value and its result will divergent because of numerical calculation error, an improved forward-backward smoothing algorithm based on square-root unscented Kalman filter （SRUKF） is presented. To guarantee the stability of the filter, the algorithm uses the covariance square root matrix instead of the covariance matrix in the process of estimation. And the algorithm utilizes backward smoothing to get a more accu- rate state estimate as an initial condition to improve the robustness of the initial value. Simula-tion results show that the algorithm has better performance, compared with UFK and SRUKF in the filterrs stability, convergence velocity, positioning precision and the robustness to the in-itial value.
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