非线性滤波算法在无源定位中的应用

Application of Non-linear Filtering Algorithms in Passive Location

针对无源定位中量测方程非线性对滤波精度及稳定性的影响,分析了基于模型线性化的滤波算法,包括扩展卡尔曼滤波(EKF)、伪线性滤波(PLF)、修订增益的扩展卡尔曼滤波(MGEKF)算法的特点,重点论述了非线性滤波(UKF)与粒子滤波器(PF)这2种新的非线性滤波方法思想及其特点,指出了无源定位问题中,这2种算法有更好的滤波精度及稳定性.

For the effect of nonlinear measurement equation in passive location on the precision and robustness of filtering, Model-linearization algorithms which include EKF algorithms, PLF algorithms, and MGEKF algo- rithms are analyzed. Then the feature and idea of two novel nonlinear filtering algorithms, UKF and PF, are emphatically discussed. It is also shown that these two algorithms are more robust and precise in passive location.