自动化学报2012，Vol.38Issue(8)：1343-1352,10.DOI:10.3724/SP.J.1004.2012.01343
高斯混合扩展目标概率假设密度滤波器的收敛性分析
Convergence Analysis of the Gaussian Mixture Extended-target Probability Hypothesis Density Filter
摘要
Abstract
The convergence of the Gaussian mixture extended-target probability hypothesis density (GM-EPHD) filter is studied. Under the assumptions that the clutter intensity is known a priori and the expected number of measurements arising from an extended target is continuous and bounded, this paper proves that the GM-EPHD filter converges uniformly to the true EPHD filter as the number of GM terms tends to infinity. In addition, this paper also proves that the extended Kalman (EK) filter approximation of the algorithm in weak nonlinear condition, which is called EK-GM-EPHD filter, converges uniformly to the true EPHD filter as the covariance of each GM term tends to zero. The purpose of this paper is to theoretically present the convergence results of the GM-EPHD and EK-GM-EPHD filters and the conditions under which they satisfy uniform convergence.关键词
扩展目标跟踪/概率假设密度滤波器/高斯混合方法/收敛性分析Key words
Extended target tracking (ETT), probability hypothesis density (PHD) filter, Gaussian mixture (GM)method, convergence analysis引用本文复制引用
连峰,韩崇昭,刘伟峰,元向辉..高斯混合扩展目标概率假设密度滤波器的收敛性分析[J].自动化学报,2012,38(8):1343-1352,10.基金项目
国家自然科学基金(61004087,91016020,61005026),中国博士后科学基金(20100481338),中央高校基本科研业务费专项资金资助 （61004087,91016020,61005026）
