文献类型：期刊文献

英文题名：Improved Joint Probabilistic Data Association (JPDA) Filter Using Motion Feature for Multiple Maneuvering Targets in Uncertain Tracking Situations

作者：Fan, En[1,2];Xie, Weixin[1];Pei, Jihong[1];Hu, Keli[2];Li, Xiaobin[3,4];Podpecan, Vid[5,6]

机构：[1]Shenzhen Univ, ATR Natl Key Lab Def Technol, Shenzhen 518060, Peoples R China;[2]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[3]Lanzhou Inst Technol, Coll Software Engn, Lanzhou 730050, Gansu, Peoples R China;[4]Jozef Stefan Int Postgrad Sch, Jamova Cesta 29, Ljubljana 1000, Slovenia;[5]Jozef Stefan Inst, Jamova Cesta 39, Ljubljana 1000, Slovenia;[6]Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, Ljubljana 1000, Slovenia

年份：2018

卷号：9

期号：12

外文期刊名：INFORMATION

收录：ESCI(收录号：WOS:000454713600030)、EI(收录号：20185206304605)、Scopus(收录号：2-s2.0-85058996675)、WOS

基金：This work was funded by the National Natural Science Foundation of China (61703280, 61603258 and 61331021), the Plan Project of Science and Technology of Shaoxing City (2017B70056), the Qizhi Talent Cultivation Project of Lanzhou Institute of Technology (2018QZ-09), the Youth Science and Technology Innovation Project of Lanzhou Institute of Technology (18K-020), the Project of Resources and Environment Informatization Gansu International Science and Technology Cooperation Base, and the Shenzhen Science and Technology Projection JCYJ20170818143547435.

语种：英文

外文关键词：multiple maneuvering target tracking; joint probabilistic data association; fuzzy recursive least square filter; information fusion

外文摘要：To track multiple maneuvering targets in cluttered environments with uncertain measurement noises and uncertain target dynamic models, an improved joint probabilistic data association-fuzzy recursive least squares filter (IJPDA-FRLSF) is proposed. In the proposed filter, two uncertain models of measurements and observed angles are first established. Next, these two models are further employed to construct an additive fusion strategy, which is then utilized to calculate generalized joint association probabilities of measurements belonging to different targets. Moreover, the obtained probabilities are applied to replace the joint association probabilities calculated by the standard joint probabilistic data association (JPDA) method. Considering the advantage of the fuzzy recursive least squares filter (FRLSF) on tracking a single maneuvering target, which can relax the restrictive assumption of measurement noise covariances and target dynamic models, FRLSF is still used to update the state of each target track. Thus, the proposed filter can not only provide the advantage of FRLSF but can also adjust the weights of measurements and observed angles in the generalized joint association probabilities adaptively according to their uncertainty. The performance of the proposed filter is evaluated in two experiments with simulation data and real data. It is found to be better than the performance of other three filters in terms of the tracking accuracy and the average run time.