计算机工程与应用Issue(2)：244-249,255,7.DOI:10.3778/j.issn.1002-8331.1402-0422
多目标跟踪中一种改进的高斯混合PHD滤波算法
Improved Gaussian mixture PHD filter for multi-target tracking
摘要
Abstract
The Gaussian mixture probability hypothesis density filter is an algorithm for estimating multiple target states in clutter. An improved algorithm is proposed to resolve the missed detection problem and enhance the accuracy of the fil-ter while tracking close proximity targets. Under Gaussian mixture assumptions, the predication and update equations of the PHD filter are modified, which effectively solve the information loss problem of missed true targets. And then depend-ing on the weights of Gaussian components which decide whether the components can be utilized to extract states, the pro-posed algorithm avoids the components which have higher weights are merged and improves the tracking performance when the targets move closely. Simulation results show that the new algorithm has advantages over the ordinary one in both the aspects of filter precision and multi-target number estimation.关键词
多目标跟踪/高斯混合概率假设密度/漏检/分量合并Key words
multi-target tracking/Gaussian mixture probability hypothesis density filter/missed detection/component merging分类
信息技术与安全科学引用本文复制引用
胡玮静,陈秀宏..多目标跟踪中一种改进的高斯混合PHD滤波算法[J].计算机工程与应用,2016,(2):244-249,255,7.基金项目
国家自然科学基金（No.61373055）。 （）
