%0 Journal Article
%T A data association algorithm of improved parallel centralized<br>改进并行集中式数据关联算法
%A ZHOU Hang
%A FENG Xin-xi
%A <br>周航
%A 冯新喜
%J 重庆邮电大学学报(自然科学版)
%D 2012
%I 
%X In order to solve the problem of the N-P hard with the increased number of targets, a novel unscented joint probabilistic data association based on centralized multisensor algorithm is proposed. First, UKF is used for the propagation of state estimate in the nonlinear system.Then the data is transmitted to the fusion center through the use of the reformative parallel centrlizad way. Finally the improvement JPDA is used to associate the data. Simulation results show the algorithm has a higher accurate rate of association in a clutter environment, and the CPU occupies a relatively short time.
%K multisensor
%K multitarget
%K unscented Kalman filter(UKF)
%K approximate multi-sensor multi-target joint probabilistic data association<br>多传感器
%K 多目标
%K 不敏卡尔曼滤波器
%K 近似概率联合数据关联
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=01BA20E8BA813E1908F3698710BBFEFEE816345F465FEBA5&cid=96E6E851B5104576C2DD9FC1FBCB69EF&jid=5C2694A2E5629ECD6B59D7B28C6937AD&aid=4561954BE0A0F99DEA604B55BED78EB7&yid=99E9153A83D4CB11&vid=B91E8C6D6FE990DB&iid=38B194292C032A66&sid=5A6705FDACED0BF9&eid=A04F01817ECB9A48&journal_id=1673-825X&journal_name=重庆邮电大学学报(自然科学版)&referenced_num=0&reference_num=0