A Superimposed Intensity Multi-sensor GM-PHD Filter for Passive Multi-target Tracking
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- 英文论文题名：A Superimposed Intensity Multi-sensor GM-PHD Filter for Passive Multi-target Tracking
- 论文作者：Jinxing Pan ; Jian Zheng ; Peinan Zhang ; Songtao Feng ; Yunfei Guo
- 英文论文作者：Jinxing Pan ; Jian Zheng ; Peinan Zhang ; Songtao Feng ; Yunfei Guo ; Automation School ; Hangzhou Dianzi University ; The 28th Research Institute of China Electronics Group Corporation
- 年：2017
- 作者机构：Automation School, Hangzhou Dianzi University;The 28th Research Institute of China Electronics Group Corporation;
- 英文论文关键词：Multi-sensor multi-target tracking ; low observable target ; probability hypothesis density ; Gaussian mixture
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：TN713;TP212
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：454k
- 原文格式：D
- 会议级别：全国
摘要
In order to address the problem of tracking multiple targets with multiple passive sensors in the presence of clutter and miss-detection, a new multi-sensor Gaussian mixture probability hypothesis density filter, together with an intensity superimposing technique, is proposed in this paper. First, a gate technique is used to choose valid sensors for each predicted Gaussian component. Second, each Gaussian component is updated with measurements from valid sensors, and the fusion intensity is obtained by adding all the local posterior intensities and the global miss-detection intensity. Last, a two-step extraction method is proposed to estimate the cardinality and states of the targets. Simulation results verify the effectiveness of the proposed method.
In order to address the problem of tracking multiple targets with multiple passive sensors in the presence of clutter and miss-detection, a new multi-sensor Gaussian mixture probability hypothesis density filter, together with an intensity superimposing technique, is proposed in this paper. First, a gate technique is used to choose valid sensors for each predicted Gaussian component. Second, each Gaussian component is updated with measurements from valid sensors, and the fusion intensity is obtained by adding all the local posterior intensities and the global miss-detection intensity. Last, a two-step extraction method is proposed to estimate the cardinality and states of the targets. Simulation results verify the effectiveness of the proposed method.
In order to address the problem of tracking multiple targets with multiple passive sensors in the presence of clutter and miss-detection, a new multi-sensor Gaussian mixture probability hypothesis density filter, together with an intensity superimposing technique, is proposed in this paper. First, a gate technique is used to choose valid sensors for each predicted Gaussian component. Second, each Gaussian component is updated with measurements from valid sensors, and the fusion intensity is obtained by adding all the local posterior intensities and the global miss-detection intensity. Last, a two-step extraction method is proposed to estimate the cardinality and states of the targets. Simulation results verify the effectiveness of the proposed method.
引文
[1]Y.Bar-Shalom,T.Kirubarajan,and X.Lin,Probabilistic data association techniques for target tracking with applications to sonar,radar and EO sensors,IEEE Aerospace&Electronic Systems Magazine,2005,20(8):37-56.
[2]Z.Radosavljevic?,D.Mus?icki,B.Kovac?evic?,and W.C.Kim,Integrated particle filter for target tracking in clutter,IET Radar Sonar Navigation,2015,9(8):1063-1069.
[3]E.Grossi,M.Lops,and L.Venturino,A novel dynamic programming algorithm for track-before-detect in radar systems,IEEE Transactions on Signal Processing,2013,61(10):2608-2619.
[4]R.Mahler,Multitarget Bayes filtering via first-order multitarget moments,IEEE Transactions on Aerospace&Electronic Systems,2003,39(4):1152-1178.
[5]B.N.Vo,S.Singh,and A.Doucet,Sequential Monte Carlo methods for multitarget filtering with random finite sets,IEEE Transactions on Aerospace&Electronic Systems,2005,41(4):1224-1245.
[6]B.N.Vo,W.K.Ma,The Gaussian mixture probability hypothesis density filter,IEEE Transactions on Signal Processing,2006,54(11):4091-4104.
[7]X.Tang,X.Chen,M.Mcdonald,and R.Mahler,A Multiple-Detection Probability Hypothesis Density Filter,IEEE Transactions on Signal Processing,2015,63(8):2007-2019.
[8]M.Y.Dehkordi,Z.Azimifar,Novel N-scan GM-PHD-based approach for multi-target tracking,IET Signal Processing,2016,10(5):493-503.
[9]R.Mahler,Approximate multisensor CPHD and PHD filters,Information Fusion(FUSION),2010 13th Conference on,Edinburgh,2010:1-8.
[10]G,Battistelli,L.Chisci,S.Morrocchi,and F.Papi,Robust multisensor multitarget tracker with application to passive multistatic radar tracking,IEEE Transactions on Aerospace&Electronic Systems,2012,48(4):3450-3472.
[11]S.J.Julier,J.K.Uhlmann,Unscented filtering and nonlinear estimation,Proceedings of the IEEE,2004,92(3):401-422.
[12]D.Schuhmacher,B.T.Vo,and B.N.Vo,A consistent metric for performance evaluation of multi-object filters,IEEE Transactions on Signal Processing,2008,56(8):3447-34.
[2]Z.Radosavljevic?,D.Mus?icki,B.Kovac?evic?,and W.C.Kim,Integrated particle filter for target tracking in clutter,IET Radar Sonar Navigation,2015,9(8):1063-1069.
[3]E.Grossi,M.Lops,and L.Venturino,A novel dynamic programming algorithm for track-before-detect in radar systems,IEEE Transactions on Signal Processing,2013,61(10):2608-2619.
[4]R.Mahler,Multitarget Bayes filtering via first-order multitarget moments,IEEE Transactions on Aerospace&Electronic Systems,2003,39(4):1152-1178.
[5]B.N.Vo,S.Singh,and A.Doucet,Sequential Monte Carlo methods for multitarget filtering with random finite sets,IEEE Transactions on Aerospace&Electronic Systems,2005,41(4):1224-1245.
[6]B.N.Vo,W.K.Ma,The Gaussian mixture probability hypothesis density filter,IEEE Transactions on Signal Processing,2006,54(11):4091-4104.
[7]X.Tang,X.Chen,M.Mcdonald,and R.Mahler,A Multiple-Detection Probability Hypothesis Density Filter,IEEE Transactions on Signal Processing,2015,63(8):2007-2019.
[8]M.Y.Dehkordi,Z.Azimifar,Novel N-scan GM-PHD-based approach for multi-target tracking,IET Signal Processing,2016,10(5):493-503.
[9]R.Mahler,Approximate multisensor CPHD and PHD filters,Information Fusion(FUSION),2010 13th Conference on,Edinburgh,2010:1-8.
[10]G,Battistelli,L.Chisci,S.Morrocchi,and F.Papi,Robust multisensor multitarget tracker with application to passive multistatic radar tracking,IEEE Transactions on Aerospace&Electronic Systems,2012,48(4):3450-3472.
[11]S.J.Julier,J.K.Uhlmann,Unscented filtering and nonlinear estimation,Proceedings of the IEEE,2004,92(3):401-422.
[12]D.Schuhmacher,B.T.Vo,and B.N.Vo,A consistent metric for performance evaluation of multi-object filters,IEEE Transactions on Signal Processing,2008,56(8):3447-34.