文献详情
A Kalman Estimation Based Rao-Blackwellized Particle Filtering for Radar Tracking
文献类型期刊
作者Liu, Jingxian[1];Wang, Zulin[2];Xu, Mai[3]
机构
通讯作者Xu, M (reprint author), Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China.
2017
期刊名称IEEE ACCESS影响因子和分区
来源信息年:2017  卷:5  页码范围:8162-8174  
5
期刊信息IEEE ACCESS影响因子和分区  ISSN:2169-3536
关键词Kalman estimation; Rao-Blackwellized particle filtering; central tendency of error
页码范围8162-8174
增刊正刊
摘要The Rao-Blackwellized particle filtering (RBPF) offers a general tracking framework with linear/nonlinear state space models, which outperforms the standard particle filtering in nonlinear and non-Gaussian tracking scenarios. Unfortunately, in conventional radar systems, the observations contain no information about the linear part of target state. In these cases, the RBPF algorithm fails to catch the real trajectories, because we cannot obtain the enough information to correctly update the linear part of target state in the tracking procedure. To overcome such an issue, this paper proposes a Kalman estimation-based BRPF (KE-BRPF) algorithm. In KE-RBPF, the correlation between linear and nonlinear parts of target state is investigated. Benefitting from such investigation, we derive a new set of formulea to present the correlation in terms of means and variances. By utilizing these formulas, our KE-RBPF algorithm correctly tracks the linear part of target state based on the nonlinear one. Finally, the simulation results verify that, our KE-RBPF performs better than other state-of-the-art tracking methods in nonlinear and non-Gaussian radar tracking scenarios, with at least 18% reduction in terms of the means and central tendency of error of tracking root-mean-square-error.
收录情况SCIE(WOS:000403140800116)  
所属部门电子信息工程学院
DOI10.1109/ACCESS.2017.2693288
百度学术A Kalman Estimation Based Rao-Blackwellized Particle Filtering for Radar Tracking
语言外文
ISSN2169-3536
人气指数59
浏览次数59
基金NSFC [61202139, 61573037, 61471022]; Fok Ying Tung Education Foundation [151061]
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