%0 Journal Article
%T Gaussian mixture particle Cardinalized probability hypothesis density based passive bearings-only multi-target tracking<br>高斯混合粒子Cardinalized概率假设密度滤波被动测角多目标跟踪
%A ZHANG Jun-gen
%A JI Hong-bing
%A <br>张俊根
%A 姬红兵
%J 控制理论与应用
%D 2011
%I 
%X When the number of targets is unknown or varies with time, multi-target state and measurements are expressed as random sets and the multi-target tracking problem is addressed by the Cardinalized probability hypothesis density(CPHD) filter, which propagates not only the probability hypothesis density(PHD) of the joint distribution but also the full probability distribution on target number. However, the CPHD can not provide a closed-form solution to the nonlinear problem occurred in the passive bearings-only multi-target tracking system. A novel Gaussian mixture particle CPHD(GMPCPHD) filter is presented in the paper. The PHD is approximated by a mixture of Gaussians, which avoids clustering in the determination of target states. In addition, Quasi-Monte Carlo integration method is introduced to approximate the prediction and update distributions of target states. Simulation results verify the effectiveness of the proposed GMPCPHD.
%K multi-target tracking
%K random sets
%K Cardinalized probability hypothesis density
%K passive bearings-only
%K Quasi-Monte Carlo<br>多目标跟踪
%K 随机集
%K Cardinalized概率假设密度
%K 被动测角
%K 拟蒙特卡罗
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=819BD8C1642C41E60AA637161957784E&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=CA4FD0336C81A37A&sid=D997634CFE9B6321&eid=286FB2D22CF8D013&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=19