中文摘要：

针对单站无源跟踪系统非线性较强、传统跟踪滤波方法收敛速度慢且容易发散的问题，提出了一种基于自适应因子化H∞滤波的单站无源跟踪算法。该算法利用sigma点转换和鲁棒H∞滤波能够减小观测方程的线性化误差和降低观测误差不确定性的特点，通过新息控制减小野值对滤波的干扰，利用比例因子和渐消因子自适应调整采样点到中心点的距离和状态预报误差的协方差，从而克服基于UT变换的H∞滤波采样时的非局部效应问题，增强了单站无源跟踪系统对噪声的鲁棒性。仿真实验结果表明，本文方法通过对UT变换进行简化，在自适应因子化的同时，算法的计算量与基于UT变换的H∞滤波基本持平，且跟踪精度优于基于UT变换的H∞滤波算法。该算法在保持高精度估计能力的同’时，具有较强的鲁棒性，是解决非线性系统状态估计问题的一种有效方法。

英文摘要：

Single observer passive target tracking based on adaptive gene H∞ filter is proposed for the highly non-linear passive location and tracking system, which means that tracking filters often failed to catch and keep tracking of the emitter. On the basis of sigma point transformation technique and H∞filtering method, the proposed algorithm can decrease linearization error of high nonlinear system and solve the noise uncertainty problem. The algorithm has a control abnormal innovations, which can efficiently restrain the unfavorable influence out of outliers, and apply the scaled factor and a fading factor to adjust the distance of sampling point to center point and the covariance matrix of estate predict error. And then the problem of non local effects of sampling can be resolved and single observer passive target tracking system is more robust to measurement error of time difference of arrival： Simulation results show that the proposed algorithm is similar to sigma point H∞filtering method in computational complexity, and the algorithm outperforms the sigma point Hoo filtering method in tracking accuracy as stability. Therefore it is more suitable to the nonlinear state estimation.