非线性滤波方法在水下目标跟踪中的应用

Application of Non-linear Filtering to Underwater Target Tracking

在对扩展卡尔曼滤波、不敏卡尔曼滤波和粒子滤波3种非线性滤波方法进行研究的基础上,对粒子滤波算法的重要性密度函数的选取方法进行了研究。结合水下目标的被动跟踪的应用背景,比较了3种滤波算法在水下目标跟踪中的性能差异。结果表明,粒子滤波算法能较好的用于非线性、非高斯条件下的水下目标跟踪。

The selection of importance density function of particle filtering has been researched after discussing three non-linear filtering：Extend Kalman filtering,Unscented Kalman filter and particle filter.Under the background of underwatertarget passive tracking,the three filtering performance has been compared.The result shows that particle filtering can deal with tracking of the underwater target very well under non-linear,non-Gauss conditions.