%0 Journal Article
%T Modified Gaussian particle probability hypothesis density filtering algorithm<br>改进的高斯粒子概率假设密度滤波算法
%A ZHOU Cheng-xing
%A LIU Gui-xi
%A HOU Lian-yong
%A ZHONG Xing-zhi
%A <br>周承兴
%A 刘贵喜
%A 侯连勇
%A 钟兴质
%J 控制理论与应用
%D 2011
%I 
%X The Gaussian particle probability hypothesis density filter needs particle approximation and resampling in the prediction step and the update step; this lowers the accuracy and deteriorates the real-time performance of the algorithm to some extent. To solve this problem, a modified Gaussian particle probability hypothesis density filtering algorithm is proposed. This algorithm expresses and transfers the predicted probability hypothesis density (PHD) of targets in the form of particles, and then directly updates these particles representing the predicted PHD. Finally, the algorithm approximates the updated PHD into a Gaussian mixture function by using the particles with greatest likelihood. The simulation experiments show that the modified algorithm reduces the multi-target error distance by nearly 30% and cuts the running time by nearly 50% in comparison with Gaussian particle probability hypothesis density filter.
%K multiple target tracking
%K random sets
%K probability hypothesis density
%K Gaussian mixture function
%K particle approximation<br>多目标跟踪
%K 随机集
%K 概率假设密度
%K 混合高斯
%K 粒子近似
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=B18D47477623D2765E3A95B5C9E4B6E4&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=DF92D298D3FF1E6E&sid=67C739DC23BADF58&eid=82BCA4C44409DD5C&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=9