一种非线性GM-PHD滤波新方法  

A Novel Gaussian Mixture PHD Filter for Nonlinear Models

作　　者：王品[1] 谢维信[1] 刘宗香[1] 李鹏飞[2] 

机构地区：[1]深圳大学ATR国防科技重点实验室,广东深圳518060 [2]防空兵指挥学院,河南郑州450052

出　　处：《电子学报》2012年第8期1597-1602,共6页Acta Electronica Sinica

基　　金：国防预研基金(No.51326030204);国家重点实验室基金(No.9140C8004011007);国家科技支撑计划项目(No.2011BAH201302;No.2011BAH20B03)

摘　　要：为了解决目标数未知情况下的多目标跟踪问题,提出了一种非线性条件下的高斯混合概率假设密度滤波新方法.该方法利用三阶球面容积-径向采样规则计算目标状态的概率分布特性,解决了状态方程和观测方程的非线性计算问题,利用模糊门限对滤波器的剪枝方法进行了优化,避免了高斯项数目的指数增长,利用观测数据生成新目标密度,使滤波器具备了对观测空间任意位置随机出现新目标的跟踪能力.通过仿真实验比较了四种非线性高斯混合概率假设密度滤波方法的性能,实验结果验证了提出算法的有效性.To solve the problem of multi-target tracking model with the time-varying number of targets, a novel Ganssian mixture PHD filter is proposed for the nonlinear Gaussian system. A third-degree Spherical-Radial rule is applied to calculate the prediction and update distributions of target states for nonlinear multi-target models. The pruning method is optimized by using a fuzzy threshold to avoid the exponential increasing of the Ganssian components. The measurements are used to generate the density of new targets that appear randomly anywhere in the observation space. The performance of the four nonlinear Gaussian Mixture PHD filters is compared. The simulation results demonstrated the efficiency of the proposed algorithm.

关 键 词：多目标跟踪 随机有限集 概率假设密度滤波器 容积卡尔曼滤波 模糊门限 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN953[自动化与计算机技术—计算机科学与技术]