Convergence Results for the Gaussian Mixture Implementation of the Extended-Target PHD Filter and Its Extended Kalman Filtering Approximation

被引：2

作者：

Lian, Feng ^{[1
]}

Han, Chongzhao ^{[1
]}

Liu, Jing ^{[1
]}

Chen, Hui ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Minist Educ, Key Lab Intelligent Networks & Network Secur MOE, Sch Elect & Informat Engn, Xian 710049, Peoples R China

来源：

JOURNAL OF APPLIED MATHEMATICS | 2012年

基金：

中国博士后科学基金;

关键词：

HYPOTHESIS DENSITY FILTER; TRACKING;

D O I：

10.1155/2012/141727

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The convergence of the Gaussian mixture extended-target probability hypothesis density (GMEPHD) filter and its extended Kalman (EK) filtering approximation in mildly nonlinear condition, namely, the EK-GM-EPHD filter, is studied here. This paper proves that both the GM-EPHD filter and the EK-GM-EPHD filter converge uniformly to the true EPHD filter. The significance of this paper is in theory to present the convergence results of the GM-EPHD and EK-GM-EPHD filters and the conditions under which the two filters satisfy uniform convergence.

引用

页数：20

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