基于幅度信息的标签多伯努利滤波算法
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摘要
杂波环境下,现有多目标跟踪滤波器会出现性能衰减。对此,提出了基于幅度信息(amplitude information,AI)的广义标签多伯努利(generalized labeled multi-Bernoulli,GLMB)滤波算法(AI-GLMB)。通常杂波幅度低于目标回波幅度,通过引入幅度信息对目标状态进行扩展,建立幅度似然函数,推导新的更新方程,并给出了算法的序贯蒙特卡罗实现方法。仿真结果表明,AI-GLMB算法能有效适应高杂波环境,同幅度信息辅助的概率假设密度滤波算法、幅度信息势平衡多伯努利滤波算法及传统GLMB滤波算法相比,其跟踪精度更高。
For the problem that the existing multiple target tracking filters can cause performance attenuation in clutter environment,an amplitude information generalized labeled multi-Bernoulli(AI-GLMB)filter is proposed.In general,the amplitude of clutter is lower than those from target returns.By introducing amplitude information to expand the target states,the amplitude likelihood function is derived.Then the new updating equation is deduced,and the sequential Monte Carlo implementation of the proposed method is given.Simulation results show that the AI-GLMB algorithm can adapt to the clutter environment effectively,and has higher tracking accuracy than the amplitude information assistant probability hypothesis density filter,amplitude information cardinality balanced multi-target multi-Bernoulli filter and the standard GLMB filter.
For the problem that the existing multiple target tracking filters can cause performance attenuation in clutter environment,an amplitude information generalized labeled multi-Bernoulli(AI-GLMB)filter is proposed.In general,the amplitude of clutter is lower than those from target returns.By introducing amplitude information to expand the target states,the amplitude likelihood function is derived.Then the new updating equation is deduced,and the sequential Monte Carlo implementation of the proposed method is given.Simulation results show that the AI-GLMB algorithm can adapt to the clutter environment effectively,and has higher tracking accuracy than the amplitude information assistant probability hypothesis density filter,amplitude information cardinality balanced multi-target multi-Bernoulli filter and the standard GLMB filter.
引文
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[21]LU Z,HU W D,KIRUBARAJAN T.Labeled random finite sets with moment approximation[J].IEEE Trans.on Signal Processing,2017,65(13):3384-3398.
[22]邱昊,黄高明,左炜,等.多模型标签多伯努利机动目标跟踪算法[J].系统工程与电子技术,2015,37(12):2683-2688.QIU H,HUANG G M,ZUO W,et al.Multiple model labeled multi-Bernoulli filter for maneuvering target tracking[J].Systems Engineering and Electronics,2015,37(12):2683-2688.
[23]WEI Y,MENG J,HOSEINNEZHAD R.The multiple model vovo filter[J].IEEE Trans.on Aerospace and Electronic Systems,2017,53(2):1045-1054.
[24]VO B N,VO B T.An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling[C]∥Proc.of the International Conference on Information Fusion,2017:1-8.
[25]PAPA G,BRACA P,HORN S,et al.Multisensor adaptive Bayesian tracking under time-varying target detection probability[J].IEEE Trans.on Aerospace and Electronic Systems,2017,52(5):2193-2209.
[26]LI S Q,YI W,HOSEINNEZHAD R,et al.Robust distributed fusion with labeled random finite sets[J].IEEE Trans.on Signal Processing,2018,66(2):278-293.
[2]WEBSTER J G.Wiley encyclopedia of electrical and electronics engineering,24volume set+supplement[J].Library Journal,2002,3(15):71.
[3]WANG Z,LI M,LU Y,et al.Efficient TR-TBD algorithm for slow-moving weak multi-targets in heavy clutter environment[J].IET Signal Processing,2017,11(4):422-428.
[4]PAPI F.Constrainedδ-GLMB filter for multi-target track-before-detect using radar measurements[C]∥Proc.of the Intelligence and Security Informatics Conference,2015:90-97.
[5]罗兴旺,张伯彦,刘嘉,等.雷达数据处理中的杂波抑制方法[J].系统工程与电子技术,2016,38(1):37-44.LUO X W,ZHANG B Y,LIU J,et al.Researches on the method of clutter suppression in radar data processing[J].Systems Engineering and Electronics,2016,38(1):37-44.
[6]LERRO D,BAR-SHALOM Y.Automated tracking with target amplitude information[C]∥Proc.of the American Control Conference,1990:2875-2880.
[7]VAN KEUK G.Multihypothesis tracking using incoherent signalstrength information[J].IEEE Trans.on Aerospace and Electronic Systems,1996,32(3):1164-1170.
[8]LA SCALA B F.Viterbi data association tracking using amplitude information[C]∥Proc.of the International Conference on Information Fusion,2004:1-5.
[9]MAHLER R.A brief survey of advances in random-set fusion[C]∥Proc.of the International Conference on Control,Automation and Information Sciences,2015:62-67.
[10]GARCIA-FERNANDEZ A F.Track-before-detect labeled multiBernoulli particle filter with label switching[J].IEEE Trans.on Aerospace and Electronic Systems,2017,52(5):2123-2138.
[11]PUNCHIHEWA Y S G,VO B T,BA N V,et al.Multiple object tracking in unknown backgrounds with labeled random finite sets[J].IEEE Trans.on Signal Processing,2018,66(11):3040-3055.
[12]CLARK D,VO B N,BA T V.Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR[J].IEEE Trans.on Signal Processing,2010,58(1):26-37.
[13]CLARK D,RISTIC B,VO B N.PHD filtering with target amplitude feature[C]∥Proc.of the International Conference on Information Fusion,2008:1-7.
[14]袁常顺,王俊,孙进平,等.一种幅度信息辅助多伯努利滤波算法[J].电子与信息学报,2016,38(2):464-471.YUAN C S,WANG J,SUN J P,et al.A multi-Bernoulli filtering algorithm using amplitude information[J].Journal of Electronics and Information Technology,2016,38(2):464-471.
[15]VO B T,VO B N.Labeled random finite sets and multi-object conjugate priors[J].IEEE Trans.on Signal Processing,2013,61(13):3460-3475.
[16]VO B N,VO B T,PHUNG D.Labeled random finite sets and the Bayes multi-target tracking filter[J].IEEE Trans.on Signal Processing,2014,62(24):6554-6567.
[17]REUTER S,VO B T,VO B N,et al.The labelled multi-Bernoulli filter[J].IEEE Trans.on Signal Processing,2014,62(12):3246-3260.
[18]HOANG H G,VO B T,VO B N.A generalized labeled multiBernoulli filter implementation using gibbs sampling[J].Computer Science,2015:1-8.
[19]JONES B A,VO B T,VO B N.Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update[C]∥Proc.of the AIAA/AAS Astrodynamics Specialist Conference,2016:1-17.
[20]VO B N,VO B T,HOANG H G.An efficient implementation of the generalized labeled multi-bernoulli filter[J].IEEE Trans.on Signal Processing,2017,65(8):1975-1987.
[21]LU Z,HU W D,KIRUBARAJAN T.Labeled random finite sets with moment approximation[J].IEEE Trans.on Signal Processing,2017,65(13):3384-3398.
[22]邱昊,黄高明,左炜,等.多模型标签多伯努利机动目标跟踪算法[J].系统工程与电子技术,2015,37(12):2683-2688.QIU H,HUANG G M,ZUO W,et al.Multiple model labeled multi-Bernoulli filter for maneuvering target tracking[J].Systems Engineering and Electronics,2015,37(12):2683-2688.
[23]WEI Y,MENG J,HOSEINNEZHAD R.The multiple model vovo filter[J].IEEE Trans.on Aerospace and Electronic Systems,2017,53(2):1045-1054.
[24]VO B N,VO B T.An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling[C]∥Proc.of the International Conference on Information Fusion,2017:1-8.
[25]PAPA G,BRACA P,HORN S,et al.Multisensor adaptive Bayesian tracking under time-varying target detection probability[J].IEEE Trans.on Aerospace and Electronic Systems,2017,52(5):2193-2209.
[26]LI S Q,YI W,HOSEINNEZHAD R,et al.Robust distributed fusion with labeled random finite sets[J].IEEE Trans.on Signal Processing,2018,66(2):278-293.