[1]康旭超,何广军,陈峰,等.密集杂波下的模糊数据关联多目标跟踪算法[J].探测与控制学报,2019,41(04):56.[doi:.]
 KANG Xuchao,HE Guangjun,CHEN Feng,et al.Fuzzy Data Associated with Multi-target Tracking Algorithm under Dense Clutter[J].,2019,41(04):56.[doi:.]
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密集杂波下的模糊数据关联多目标跟踪算法()
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《探测与控制学报》[ISSN:1008-1194/CN:61-1316/TJ]

卷:
41
期数:
2019年04
页码:
56
栏目:
出版日期:
2019-08-26

文章信息/Info

Title:
Fuzzy Data Associated with Multi-target Tracking Algorithm under Dense Clutter
文章编号:
1008-1194(2019)04-0056-06
作者:
康旭超何广军陈峰何其芳
空军工程大学防空反导学院,陕西 西安 710051
Author(s):
KANG Xuchao HE Guangjun CHEN Feng HE Qifang
Air and Missile Defense College, Air Force Engineering University, Xi’an 710051,China
关键词:
模糊聚类多目标跟踪数据关联卡尔曼滤波
Keywords:
fuzzy clustering multi-target tracking data association Kalman filter
分类号:
TN953
DOI:
.
文献标志码:
A
摘要:
针对密集杂波环境下对多目标跟踪的精度低、实时性不强的问题,提出了密集杂波下模糊聚类数据关联多目标跟踪算法。该算法利用模糊聚类,得到不同观测量相对目标的隶属度作为模糊关联概率,通过分析公共观测对目标的影响,引入远近距下的公共观测影响因子重建模糊关联概率矩阵;然后结合模糊关联概率与卡尔曼滤波,对不同观测量得到的状态估计加权融合,从而对每个目标进行单独跟踪,实现目标的状态更新。仿真结果表明,杂波密集环境下该算法在能够保证多目标跟踪实时性的同时引入远近距下公共影响因子对不同观测量的状态估计进行加权,保证了目标跟踪的精确性。
Abstract:
Aiming at the problem of low precision, large amount of calculation and low real-time performance for multi-target tracking in dense clutter environment, a multi-target tracking algorithm based on fuzzy clustering data under dense clutter was proposed. The algorithm used fuzzy clustering to obtain the degree of membership of different target relative observations as the fuzzy association probability. Through the analysis of the influence of public observations on the target, the public observation impact factors under long-distance distance to reconstruct fuzzy associated probability matrices was introduced. Then, combining the fuzzy association probability and the Kalman filter, the state estimations obtained from different observations were weighted and fused, so that each target was separately tracked and the state of the target was updated. The simulation results showed that the algorithm could reduce the real-time performance of multi-target tracking while introducing the long-distance distance common impact factor to weight the state estimation of different observations, which ensured the accuracy of target tracking.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-12-15
基金项目:国家自然科学基金项目资助(61703424)
作者简介:康旭超(1994—),男,山东招远人,硕士研究生,研究方向:导航、跟踪与滤波。E-mail:841454491@qq.com。
更新日期/Last Update: 2019-09-12