It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random...It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian （VB） approximation is proposed. The variational approximation technique is introduced to the labeled multi-Bernoulli （LMB） filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density （VB-PHD） filter and the VB cardinality balanced multi-target multi-Bernoulli （VB-CBMeMBer） filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.展开更多
The coalescence and missed detection are two key challenges in Multi-Target Tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally diffi...The coalescence and missed detection are two key challenges in Multi-Target Tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally difficult to handle the above problems simultaneously,such as the Track-Oriented marginal Multi-Bernoulli/Poisson(TOMB/P)and Measurement-Oriented marginal Multi-Bernoulli/Poisson(MOMB/P)filters.Based on the Arithmetic Average(AA)fusion rule,this paper proposes a novel fusion framework for the Poisson Multi-Bernoulli(PMB)filter,which integrates both the advantages of the TOMB/P filter in dealing with missed detection and the advantages of the MOMB/P filter in dealing with coalescence.In order to fuse the different PMB distributions,the Bernoulli components in different Multi-Bernoulli(MB)distributions are associated with each other by Kullback-Leibler Divergence(KLD)minimization.Moreover,an adaptive AA fusion rule is designed on the basis of the exponential fusion weights,which utilizes the TOMB/P and MOMB/P updates to solve these difficulties in MTT.Finally,by comparing with the TOMB/P and MOMB/P filters,the performance of the proposed filter in terms of accuracy and efficiency is demonstrated in three challenging scenarios.展开更多
We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are dis...We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.展开更多
多目标检测与估计是多普勒雷达的基本任务。当信噪比较低时,为确保检测到目标需降低门限而产生了大量虚警,基于数据的多假设跟踪(Multi-Hypothesis Tracking,MHT)和联合概率数据关联(Joint Probabilistic Data Association,JPDA)方法因...多目标检测与估计是多普勒雷达的基本任务。当信噪比较低时,为确保检测到目标需降低门限而产生了大量虚警,基于数据的多假设跟踪(Multi-Hypothesis Tracking,MHT)和联合概率数据关联(Joint Probabilistic Data Association,JPDA)方法因计算复杂度过高而失效,基于原始信号的随机有限集(Random Finite Set,RFS)滤波器可有效解决该问题。多普勒雷达回波信号以叠加的方式受到多个目标影响,其多目标检测与估计问题属于叠加式传感器的典型应用。本文在叠加式多伯努利(Multi-Bernoulli,MBR)滤波器基础上利用具有准确势估计的独立同分布群(Independent and Identically Distributed Cluster,IIDC)RFS对新生目标建模,并采用辅助粒子滤波器(Auxiliary Particle Filter,APF)实现了多目标联合检测与状态估计。仿真结果表明,混合MBR和集势概率假设密度(Cardinalized Probability Hypothesis Density,CPHD)滤波器对多普勒雷达多目标的检测估计性能优于MBR滤波器,且减小了计算复杂度。展开更多
A novel algorithm that combines the generalized labeled multi-Bernoulli(GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments, emitter features(EFs...A novel algorithm that combines the generalized labeled multi-Bernoulli(GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments, emitter features(EFs) are often unknown and time-varying. Aiming at the unknown feature problem, we propose a method for identifying EFs based on dynamic clustering of data fields. Because EFs are time-varying and the probability distribution is unknown, an improved fuzzy C-means algorithm is proposed to calculate the correlation coefficients between the target and measurements, to approximate the EF likelihood function. On this basis, the EF likelihood function is integrated into the recursive GLMB filter process to obtain the new prediction and update equations.Simulation results show that the proposed method can improve the tracking performance of multiple targets,especially in heavy clutter environments.展开更多
This paper presents a multi-Bernoulli filter for tracking the direction of arrival(DOAs)of time-varying number of targets using sensor array.Our method operates directly on the measurements of sensor array and does no...This paper presents a multi-Bernoulli filter for tracking the direction of arrival(DOAs)of time-varying number of targets using sensor array.Our method operates directly on the measurements of sensor array and does not require any detection.Firstly,more information is reserved and compared with the after-detection measurements using a finite set of detected points.It can significantly improve the tracking performance,especially in low signal-to-noise ratio.Secondly,it inherits the advantages of the multi-Bernoulli approximation which models each of the targets individually.This allows more accurate multi-target state estimation,especially when targets cross.The proposed filter does not need clustering step and simulation results showcase the improved performance of the proposed filter.展开更多
针对复杂不确定性环境下具有不规则形状的多扩展目标跟踪问题,提出了一种基于星凸形随机超曲面模型(Starconvex RHM)的多扩展目标多伯努利滤波算法.首先,在有限集统计(Finite set statistics,FISST)理论框架下,采用多伯努利随机有限集(M...针对复杂不确定性环境下具有不规则形状的多扩展目标跟踪问题,提出了一种基于星凸形随机超曲面模型(Starconvex RHM)的多扩展目标多伯努利滤波算法.首先,在有限集统计(Finite set statistics,FISST)理论框架下,采用多伯努利随机有限集(MBer-RFS)和泊松RFS(Possion-RFS)分别描述多扩展目标的状态和观测,并给出扩展目标势均衡多目标多伯努利(ET-CBMeMBer)滤波器.其次,利用RHM去描述任意星凸形扩展目标的量测源分布,提出了容积卡尔曼高斯混合星凸形多扩展目标多伯努利滤波器.此外,本文给出了一种多扩展目标不规则形状估计性能的评价指标.最后,通过多扩展目标和具有形状突变的多群目标的跟踪仿真实验验证了本文方法的有效性.展开更多
针对标准标签多伯努利(labeled multi-Bernoulli,LMB)算法只考虑了单个运动模型的问题,提出了一种适用于跳转马尔科夫系统的多模型标签多伯努利(multiple model LMB,MM-LMB)算法。首先对目标状态进行扩展,将多模型思想引入LMB算法得到...针对标准标签多伯努利(labeled multi-Bernoulli,LMB)算法只考虑了单个运动模型的问题,提出了一种适用于跳转马尔科夫系统的多模型标签多伯努利(multiple model LMB,MM-LMB)算法。首先对目标状态进行扩展,将多模型思想引入LMB算法得到了新的预测和更新方程,并给出了算法的序贯蒙特卡罗实现。仿真实验表明,MM-LMB算法能对多机动目标进行有效跟踪,在复杂探测环境下跟踪精度优于多模型概率假设密度(multiple model probability hypothesis density,MM-PHD)算法和多模型势平衡多目标多伯努利(multiple model cardinality balanced multi-target multi-Bernoulli,MM-CBMeMBer)算法;所提算法计算量当目标相距较远时低于MM-PHD和MM-CBMeMBer,目标聚集时增长速度快于对比算法。展开更多
在许多多目标跟踪场景中,目标返回的幅度通常强于虚警杂波返回的幅度。通过建立更加准确的包含幅度信息的目标和虚警杂波似然函数,可提高多目标估计精度。该文提出一种基于随机有限集的幅度信息辅助多伯努利滤波(Amplitude Information ...在许多多目标跟踪场景中,目标返回的幅度通常强于虚警杂波返回的幅度。通过建立更加准确的包含幅度信息的目标和虚警杂波似然函数,可提高多目标估计精度。该文提出一种基于随机有限集的幅度信息辅助多伯努利滤波(Amplitude Information Assistant Multi-Bernoulli Filter,AIA-MBer F)算法。该算法通过建立幅度似然函数将幅度信息引入到多伯努利滤波的更新过程中,并给出针对线性和非线性模型的高斯混合(Gaussian Mixture,GM)和序贯蒙特卡洛(Sequential Monte Carlo,SMC)实现方法。仿真结果表明,该滤波算法相比于传统多伯努利滤波(Multi-Bernoulli Filter,MBer F)无论GM还是SMC实现都可获得更加准确稳定的目标数和对应的目标状态估计。展开更多
针对杂波条件下可分辨群目标的状态估计、目标个数与子群个数估计问题,提出了一种基于标签随机有限集(Label random finite set,L-RFS)框架下的可分辨群目标跟踪算法,该算法主要包括两个方面:可分辨多群目标动态建模和多群目标的跟踪估...针对杂波条件下可分辨群目标的状态估计、目标个数与子群个数估计问题,提出了一种基于标签随机有限集(Label random finite set,L-RFS)框架下的可分辨群目标跟踪算法,该算法主要包括两个方面:可分辨多群目标动态建模和多群目标的跟踪估计.本文工作主要包括:1)结合图论中的邻接矩阵对可分辨群目标运动进行动态建模.2)利用基于L-RFS的广义标签多伯努利滤波(Generalizes label multi-Bernoulli,GLMB)算法对目标的状态和个数进行估计,并且通过估计邻接矩阵得到群的结构和个数估计.3)通过个数不同、结构不同的三个子群目标在二维平面分别做线性和非线性运动进行算法验证.仿真分析表明本文算法能够准确估计出群目标中各目标的状态、个数以及子群的个数,并且能获得目标的航迹估计.展开更多
基金supported by the National High Technology Research and Development Program of China (No.2014AA7014061)the National Natural Science Foundation of China (No.61501484)
文摘It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian （VB） approximation is proposed. The variational approximation technique is introduced to the labeled multi-Bernoulli （LMB） filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density （VB-PHD） filter and the VB cardinality balanced multi-target multi-Bernoulli （VB-CBMeMBer） filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.
基金supported by the National Natural Science Foundation of China(No.61871301)。
文摘The coalescence and missed detection are two key challenges in Multi-Target Tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally difficult to handle the above problems simultaneously,such as the Track-Oriented marginal Multi-Bernoulli/Poisson(TOMB/P)and Measurement-Oriented marginal Multi-Bernoulli/Poisson(MOMB/P)filters.Based on the Arithmetic Average(AA)fusion rule,this paper proposes a novel fusion framework for the Poisson Multi-Bernoulli(PMB)filter,which integrates both the advantages of the TOMB/P filter in dealing with missed detection and the advantages of the MOMB/P filter in dealing with coalescence.In order to fuse the different PMB distributions,the Bernoulli components in different Multi-Bernoulli(MB)distributions are associated with each other by Kullback-Leibler Divergence(KLD)minimization.Moreover,an adaptive AA fusion rule is designed on the basis of the exponential fusion weights,which utilizes the TOMB/P and MOMB/P updates to solve these difficulties in MTT.Finally,by comparing with the TOMB/P and MOMB/P filters,the performance of the proposed filter in terms of accuracy and efficiency is demonstrated in three challenging scenarios.
基金Project supported by the National Natural Science Foundationof China(Nos.61174142,61222310,and 61374021)the Specialized Research Fund for the Doctoral Program of Higher Education of China(Nos.20120101110115 and 20130101110109)+3 种基金theZhejiang Provincial Science and Technology Planning Projects ofChina(No.2012C21044)the Marine Interdisciplinary ResearchGuiding Funds for Zhejiang University(No.2012HY009B)theFundamental Research Funds for the Central Universities(No.2014XZZX003-12)the Aeronautical Science Foundation ofChina(No.20132076002)
文摘We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.
基金Project supported by the National Major Research and Development Project of China (No. 2018YFE0206500)the National Natural Science Foundation of China (No. 62071140)+1 种基金the International Scientific and Technological Cooperation Program of China (No. 2015DFR10220)the Technology Foundation for Basic Enhancement Plan,China (No. 2021-JCJQ-JJ-0301)。
文摘A novel algorithm that combines the generalized labeled multi-Bernoulli(GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments, emitter features(EFs) are often unknown and time-varying. Aiming at the unknown feature problem, we propose a method for identifying EFs based on dynamic clustering of data fields. Because EFs are time-varying and the probability distribution is unknown, an improved fuzzy C-means algorithm is proposed to calculate the correlation coefficients between the target and measurements, to approximate the EF likelihood function. On this basis, the EF likelihood function is integrated into the recursive GLMB filter process to obtain the new prediction and update equations.Simulation results show that the proposed method can improve the tracking performance of multiple targets,especially in heavy clutter environments.
文摘This paper presents a multi-Bernoulli filter for tracking the direction of arrival(DOAs)of time-varying number of targets using sensor array.Our method operates directly on the measurements of sensor array and does not require any detection.Firstly,more information is reserved and compared with the after-detection measurements using a finite set of detected points.It can significantly improve the tracking performance,especially in low signal-to-noise ratio.Secondly,it inherits the advantages of the multi-Bernoulli approximation which models each of the targets individually.This allows more accurate multi-target state estimation,especially when targets cross.The proposed filter does not need clustering step and simulation results showcase the improved performance of the proposed filter.
文摘针对复杂不确定性环境下具有不规则形状的多扩展目标跟踪问题,提出了一种基于星凸形随机超曲面模型(Starconvex RHM)的多扩展目标多伯努利滤波算法.首先,在有限集统计(Finite set statistics,FISST)理论框架下,采用多伯努利随机有限集(MBer-RFS)和泊松RFS(Possion-RFS)分别描述多扩展目标的状态和观测,并给出扩展目标势均衡多目标多伯努利(ET-CBMeMBer)滤波器.其次,利用RHM去描述任意星凸形扩展目标的量测源分布,提出了容积卡尔曼高斯混合星凸形多扩展目标多伯努利滤波器.此外,本文给出了一种多扩展目标不规则形状估计性能的评价指标.最后,通过多扩展目标和具有形状突变的多群目标的跟踪仿真实验验证了本文方法的有效性.
文摘针对标准标签多伯努利(labeled multi-Bernoulli,LMB)算法只考虑了单个运动模型的问题,提出了一种适用于跳转马尔科夫系统的多模型标签多伯努利(multiple model LMB,MM-LMB)算法。首先对目标状态进行扩展,将多模型思想引入LMB算法得到了新的预测和更新方程,并给出了算法的序贯蒙特卡罗实现。仿真实验表明,MM-LMB算法能对多机动目标进行有效跟踪,在复杂探测环境下跟踪精度优于多模型概率假设密度(multiple model probability hypothesis density,MM-PHD)算法和多模型势平衡多目标多伯努利(multiple model cardinality balanced multi-target multi-Bernoulli,MM-CBMeMBer)算法;所提算法计算量当目标相距较远时低于MM-PHD和MM-CBMeMBer,目标聚集时增长速度快于对比算法。
