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高斯混合粒子PHD滤波被动测角多目标跟踪 被引量：12

Gaussian mixture particle probability hypothesis density based passive bearings-only multi-target tracking
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摘要 为解决目标数未知或随时间变化的多目标跟踪问题,通常将多目标状态和观测数据表示成随机集形式,并通过递推计算目标状态联合分布的概率假设密度(PHD)来完成.然而,对于被动测角的非线性跟踪问题,PHD无法获得闭合解,为此提出一种新的高斯混合粒子PHD算法.该算法利用高斯混合近似PHD,以避免用聚类确定目标状态,并采用拟蒙特卡罗(QMC)积分方法计算目标状态的预测和更新分布.仿真结果验证了所提出算法的有效性. When the number of targets is unknown or varies with time, multi-target state and measurements are represented as random sets and the multi-target tracking problem is addressed by calculating the probability hypothesis density（PHD） of the joint distribution, recursively. However, PHD can not provide a closed-form solution to the nonlinear problem occurred in the passive bearings-only multi-target tracking system. A new Gaussian mixture particle PHD（GMPPHD） filter is presented in the paper. The PHD is approximated by a mixture of Gaussians, which avoids clustering to determine target states. And Quasi-Monte Carlo integration method is used for approximating the prediction and update distributions of target states. Simulation results show the effectiveness of the proposed algorithm.
作者 张俊根 姬红兵
机构地区 西安电子科技大学电子工程学院
出处 《控制与决策》 EI CSCD 北大核心 2011年第3期413-417,共5页 Control and Decision
基金 国家自然科学基金项目(60871074).
关键词 多目标跟踪 随机集 概率假设密度 被动测角 拟蒙特卡罗积分 multi-target tracking random sets： probability hypothesis density： passive bearings-only Quasi-Monte Carlo integration
分类号 TN953 [电子电信—信号与信息处理]
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参考文献15

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共引文献16

同被引文献115

  • 1陈俊超,张俊豪,刘诗佳,陆小锋.基于背景建模与帧间差分的目标检测改进算法[J].计算机工程,2011,37(S1):171-173. 被引量：23
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引证文献12

二级引证文献91

控制与决策
2011年 第3期

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