EK-GMPHD滤波算法  

EK-GMPHD Filter Algorithm

作　　者：赵斌[1] 胡建旺[1] 吉兵[1] 

机构地区：[1]军械工程学院信息工程系,石家庄050003

出　　处：《电光与控制》2015年第11期84-88,共5页Electronics Optics & Control

摘　　要：针对杂波环境下多目标数目时变的跟踪问题,提出了一种适用于非线性系统的扩展卡尔曼-高斯混合概率假设密度滤波算法(EK-GMPHD)。对高斯分量进行递推时,利用扩展卡尔曼滤波器思想进行局部线性化,解决了量测方程和状态方程的非线性问题;在缩减高斯项数目时,建立了一种新的合并准则,综合考虑了高斯分量协方差对估计精度的影响;利用当前时刻目标估计数目对前一时刻的目标估计数目进行平滑,消除了孤立点的影响;仿真结果表明,该算法可有效滤除杂波影响,准确估计多目标数目和状态。For the problem of time-varying multi-target tracking in clutter environment, the Extended Kalman-Gaussian Mixture Probability Hypothesis Density （EK-GMPHD） filter algorithm applicable to non- linear system is presented. In recursion of Gaussian component, the extended Kalman filter is used for local linearization, which solves the non-linear problem of measurement equation and state equation. In reducing the number of Gaussian terms, a new merge criterion is established, which gives consideration to the influence of Gaussian component covariance on the estimation accuracy. The estimated number of targets at the current moment is used to smooth the estimated number of targets at the previous moment, thus can eliminate the influence of outlier. Simulation results show that the proposed algorithm can effectively filter out clutter, and accurately estimate the number and state of multiple targets.

关 键 词：多目标跟踪 概率假设密度 高斯混合 扩展卡尔曼 合并准则 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]