被动定位跟踪中的非线性滤波技术

针对被动定位跟踪中状态空间模型非线性程度较高所引发的滤波精度偏低的问题,分析和总结了已有的包括推广卡尔曼滤波(EKF)、修正增益的推广卡曼滤波(MGEKF)、二阶滤波、自适应推广卡尔曼滤波(AEKF)等各种次优递推滤波算法的特点。在此基础上重点论述了一种基于贝叶斯原理的序贯蒙特卡罗粒子滤波技术,该方法通过粒子的加权和表征后验概率密度,获得状态估值,在处理非线性非高斯系统的状态估计问题时精度逼近最优,鲁棒性更好。

Nonlinear filtering techniques for passive locating and tracking

For large errors introduced by nonlinear state-space model in passive locating and tracking problems,various suboptimal recursive filtering algorithms are aralyzed and summarized, such as the extended Kalman filtering(EKF),the modified gain extended Kalman filtering(MGEKF),the second order filtering and the adaptive extended Kalman filtering(AEKF). On this basis a nonlinear filtering technique of sequential Monte Carlo particle filter based on Bayesian approach is emphatically disussed which the posterior distribution of the state variables can be represented by a set of weighted particles, so the method hase advantages over the above algorithms in robustness and accuracy for nonlinear non-Gaussian filtering problems.