太赫兹科学与电子信息学报2017，Vol.15Issue(3)：382-387,6.DOI:10.11805/TKYDA201703.0382
基于GM-PHD的多目标跟踪算法仿真及影响因素
Simulation of multi-target tracking based on the GM-PHD filter and influence factors analysis
摘要
Abstract
The multi-target tracking techniques based on Finite Set Statistics(FISST) possess strict foundation of Bayesian theory, and it can simultaneously complete the estimations of the number of targets and their corresponding kinematic states, meanwhile the difficulties caused by traditional data association are avoided. The Gaussian Mixture Probability Hypothesis Density(GM-PHD) filter is utilized to track multi-target whose number is time-varying. The number of the targets is estimated in every moment by using this algorithm. On this basis, the influences of survival probabilityps, detection probabilitypd and clutter densityλc on the results are analyzed. The analysis results provide beneficial reference to the parameter selection of GM-PHD filter in practical applications.关键词
高斯混合概率假设密度滤波器/检测概率/存活概率/杂波密度/最优子模式分配距离Key words
Gaussian Mixture Probability Hypothesis Density(GM-PHD)/detection probability/survival probability/clutter density/Optimal Subpattern Assignment(OSPA) distance分类
信息技术与安全科学引用本文复制引用
赵一倩,朱红鹏,孙璐,柳超..基于GM-PHD的多目标跟踪算法仿真及影响因素[J].太赫兹科学与电子信息学报,2017,15(3):382-387,6.基金项目
国家自然科学基金资助项目(61501487 （）
61471382 （）
61401495 （）
61201445 （）
61179017) （）
山东省自然科学基金资助项目(2015ZRA06052) （2015ZRA06052）
飞行器海上测量与控制联合实验室开放基金和"泰山学者"建设工程专项经费资助项目 （）
