Effective implementation and improvement of fast labeled multi-Bernoulli filter

Effective implementation of the fast labeled multi-Bernoulli(FLMB)filter is addressed for target tracking with interval measurements.Firstly,a sequential Monte Carlo(SMC)implementation of the FLMB filter,SMC-FLMB filter,is derived based on generalized likelihood function weighting.Then,a box particle(BP)implementation of the FLMB filter,BP-FLMB filter,is developed,with a computational complexity reduction of the SMC-FLMB filter.Finally,an improved version of the BP-FLMB filter,improved BP-FLMB(IBP-FLMB)filter,is proposed,improving its estimation accuracy and real-time performance under the conditions of low detection probability and high clutter.Simulation results show that the BP-FLMB filter has a great improvement of the real-time performance than the SMC-FLMB filter,with similar tracking performance.Compared with the BP-FLMB filter,the IBP-FLMB filter has better estimation performance and real-time performance under the conditions of low detection probability and high clutter.

Multi-Bernoulli Filter for Tracking Multiple Targets Using Sensor Array

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.

A robust Poisson multi-Bernoulli filter for multi-target tracking based on arithmetic average fusion

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.

基于联合GLMB滤波器的可分辨群目标跟踪

针对联合广义标签多伯努利(joint generalized labeled multi-Bernoulli, J-GLMB)滤波算法中群目标之间距离较近、容易关联错误的问题,结合超图匹配(hypergraph matching, HGM)提出一种基于HGM-J-GLMB滤波器的可分辨群目标跟踪算法。首先,采用J-GLMB滤波器估计群内各目标的状态、数目及轨迹信息,并利用HGM结果提升量测与预测状态之间的关联性能。其次,通过图理论计算邻接矩阵,获取群结构信息和子群数目。随后,利用群结构信息估计协作噪声,进而校正目标的预测状态。最后,通过平滑算法改善滤波效果,并设置轨迹长度阈值,使其在平滑状态达到消除短轨迹的目的。仿真实验表明,所提算法在线性系统下能有效提升群目标跟踪性能。

非线性量测下的机动多目标跟踪

为了解决非线性量测下机动多目标跟踪实时性差、跟踪误差大以及对杂波变化鲁棒性较差的问题,基于随机有限集理论,提出了一种采用量测转换和模糊算法改进的多模型δ-广义标签多伯努利滤波器。首先,推导了交互多模型的δ-GLMB滤波器,通过去相关无偏量测转换实现位置量测从极坐标系到笛卡尔坐标系的无偏转换,并通过预测值去除量测误差和其协方差的相关性造成的滤波估计偏差,实现了非线性场景下的机动多目标跟踪;然后,通过航迹和量测的关联新息以及目标的机动约束构建联合波门,降低了杂波量测的数量;最后引入改进的模糊算法,以目标的模型后验概率为输入,根据模型的分离程度自适应调节运动模型的过程噪声,增加滤波精度。研究结果表明:在杂波环境下,通过与CKF-JMS-δ-GLMB、CKF-IMM-δ-GLMB等非线性多模型滤波器对比,所提算法计算时间较小,且跟踪精度更高,鲁棒性强。所提算法避免了传统的非线性处理方式计算量较大的问题,并且具有较好的杂波抑制特性,提升了非线性量测下机动多目标跟踪的性能。

面向非线性MTT的多模型泊松多伯努利混合滤波算法

在多目标跟踪(Multi-target Tracking,MTT)的非线性特性与低检测概率情况下,针对多伯努利滤波算法的高斯混合(Gaussian Mixture,GM)实现难以精确估计目标的势与运动状态的实际问题,本文提出了一种适用于非线性系统的泊松多伯努利混合滤波(Poisson Multi-Bernoulli Mixture Filter,PMBM)算法.首先,推导出多模型泊松多伯努利混合滤波的高斯混合(GM Multi-model PMBM,GM-MM-PMBM)实现过程.然后,分别对GM-MM-PMBM的伯努利高斯分量进行预测与更新,实现了基于非线性系统的MTT.为提升系统稳定性,基于平方根协方差矩阵推导出GM-MM-PMBM均方根容积卡尔曼滤波算法的实现过程.最后,仿真实验综合验证了本文算法的跟踪性能.

机动目标跟踪的交互多模型泊松多伯努利混合滤波

满足共轭先验性质的泊松多伯努利混合(Poisson multi-Bernoulli mixture,PMBM)滤波器将目标状态分为泊松和多伯努利混合两部分,分别对这两部分进行预测和更新,具有较高的跟踪精度和较快的运行速度。在多目标机动场景下,使用单一模型不足以描述目标的运动,将导致跟踪性能的下降。针对这一问题,提出了一种交互多模型(interacting multiple model,IMM)PMBM滤波器,充分利用模型之间的交互信息,可以有效实现多机动目标的跟踪。同时,该算法采用序贯蒙特卡罗(sequential Monte Carlo,SMC)方法实现PMBM滤波,可应用于非线性场景。仿真结果表明,所提的IMM-SMC-PMBM算法可以有效地在非线性环境下跟踪数目变化的多机动目标,与IMM-SMC概率假设密度(probability hypothesis density,PHD)滤波器相比具有更好的跟踪精度和稳定性。

Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics

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.

An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering

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.

基于标签多伯努利跟踪器的对手风险动态评估方法

在诸多的军事和民用领域都存在对手目标蓄意入侵我方重要区域从事恶意伤害活动的场景。对手风险评估是基于我方传感器获取的量测数据,在线评估和预测对手行动对我方资产造成的潜在伤害和损失。为了评估随机且动态变化的对手风险,该文提出一种基于标签多伯努利(LMB)跟踪器的统计对手风险动态评估方法。首先,在LMB跟踪器的框架下,基于加性模型和乘性模型,分别推导了统计对手风险最小均方误差估计的表达式。其次,针对所涉及的非线性函数积分问题,结合混合高斯近似和抽样近似方法,提出统计对手风险最小均方误差估计的数值计算方法;最后,将统计对手风险估计方法与LMB跟踪器的迭代过程有机结合,可实现入侵的多目标对我方重要资产期望损失的动态在线评估。模拟多个具有杀伤能力的目标攻击我方雷达阵地的场景,利用雷达获取的实时点迹量测数据,验证了提出算法的有效性和性能优势。

Generalized labeled multi-Bernoulli filter with signal features of unknown emitters

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.

基于IST-RSCKF-MB的雷达多目标跟踪算法

针对多目标跟踪(MTT)算法存在较大的计算量问题,将改进渐消因子的强跟踪(IST)引入快速平方根容积卡尔曼滤波(RSCKF)中,并联合新息自相关矩阵和Murty算法确定最佳假设的多伯努利(MB)算法,提出改进强跟踪平方根容积卡尔曼多伯努利(IST-RSCKF-MB)的雷达多目标跟踪算法。仿真结果显示,所提出算法的运算效率和滤波精度比平方根容积卡尔曼多伯努算法、改进强跟踪平方根容积卡尔曼多伯努利混合算法、扩展卡尔曼多伯努利算法和无迹卡尔曼多伯努利算法均有不同程度提高,误差率分别减少0.36%、4.71%、14.75%和0.17%,适用于嵌入式目标跟踪算法实现。

任意不等跨等截面连续梁自由振动分析

以等截面任意不等跨连续梁为对象,基于Euler-Bernoulli梁理论,利用分段梁模型和分离变量法,在考虑连续梁边界以及梁段位移连续性条件的基础上,提出了任意多跨不等跨连续梁固有频率和振动模态分析新方法。利用该方法可高效得到任意不等跨连续梁的固有频率和振动模态,该方法将为多跨连续梁车-桥耦合等受迫振动响应研究提供新的思路和便利。

基于多伯努利滤波的厚尾噪声条件下多扩展目标跟踪

针对厚尾噪声条件下不规则星凸形多扩展目标跟踪(Multiple extended target tracking,METT)问题,提出一种基于多伯努利滤波的厚尾噪声条件下多扩展目标跟踪方法.首先,采用学生t分布对厚尾过程噪声和量测噪声进行建模,并基于有限集统计(Finite set statistics,FISST)理论利用随机超曲面模型(Random hypersurface model,RHM)建立不规则星凸形多扩展目标的跟踪滤波模型.然后,利用学生t混合(Student's t mixture,STM)模型来表征多伯努利密度,提出学生t混合多扩展目标多伯努利(Student's t mixture multiple extended target multi-Bernoulli filter,STM-MET-CBMeMBer)滤波算法,并进一步基于鲁棒学生t容积滤波算法提出了非线性鲁棒学生t混合星凸形多扩展目标多伯努利滤波算法.最后,通过构造厚尾噪声条件下星凸形多扩展目标和多群目标的跟踪仿真实验验证了所提方法的有效性.

模糊推理优化的抗遮挡PMBM跟踪算法

目标遮挡是多扩展目标跟踪中的常见问题,当目标之间的距离较近或传感器的扫描范围内存在未知障碍物时,就会出现目标被部分或全部遮挡的现象,从而导致对目标的漏估。针对现有的泊松多伯努利混合滤波算法在遮挡场景下不能稳定跟踪的问题,提出了融入模糊推理的高斯过程-泊松多伯努利混合滤波算法。首先,在随机集目标跟踪框架下根据不同的遮挡场景给出了对应的扩展目标遮挡模型;在此基础上对高斯过程-泊松多伯努利混合滤波器的状态空间进行扩维,通过加入可变检测概率的方式将遮挡对目标状态的影响考虑到算法滤波步骤中;最后构建了可以估计目标遮挡概率的模糊推理系统,并将其与高斯过程-泊松多伯努利混合滤波算法结合,借助模糊系统的描述能力和泊松多伯努利混合滤波器良好的跟踪性能,实现遮挡场景下对目标的准确估计。仿真实验结果表明,所提算法在目标遮挡场景下的跟踪性能优于现有的泊松多伯努利混合滤波算法。

多普勒雷达下的机动多目标跟踪算法

针对多普勒雷达在跟踪机动多目标过程中由于多普勒盲区(DBZ)造成量测丢失、对跟踪器性能产生严重影响这一问题,提出将最小可检测速度(MDV)带入到交互多模型广义标签多伯努利(IMM-GLMB)滤波器中,利用MDV信息抑制DBZ对跟踪器的影响。首先,通过采用基于马尔科夫分支合并策略的交互多模型(IMM)算法,解决单一运动模型的情况下无法跟踪机动目标的问题;其次,将并入MDV信息的检测概率模型带入IMM-GLMB滤波器的更新方程中,并给出了详细实现过程,利用MDV和多普勒信息来改善跟踪器性能;最后,面对目前算法需要固定航迹起始位置才可以进行跟踪的问题,提出了一种适用于广义标签多伯努利(GLMB)滤波器的自适应航迹起始算法。仿真结果表明,所提出的滤波算法在不同宽度的DBZ下都具有更好的性能表现,尤其在DBZ较小时,对滤波器的性能基本没有影响,并且所提算法在单步运行时间上有34%的提升。

多阵列水下多目标跟踪的分布式算法研究

针对水下单水听器阵列探测范围受限、目标定位和跟踪性能不足问题,论文提出一种基于匹配场定位量测模型的分布式多目标联合定位与跟踪方法。在各阵列节点选取匹配场模糊函数大于设定阈值的峰对应的坐标作为量测,结合势平衡多伯努利(Cardinality Balanced Multi-Bernoulli,CBMB)滤波,滤除噪声干扰,解决常规匹配场定位方法低信噪比导致的跟踪精度下降的影响。在分布式网络架构下,利用广义协方差交集(Generalized Covariance Intersection,GCI)融合法则,序贯融合每个阵列节点与其邻近节点各自滤波后的多目标后验概率密度,以充分利用不同阵列节点的量测信息,提高水下多目标的跟踪精度。由于融合的是多目标后验概率密度而非量测集本身,改善了集中式融合处理的高通信负担问题。仿真实验结果证明,与单水听器阵列目标跟踪算法相比,经分布式融合后,多次蒙特卡洛实验下的平均最优子模式分配(Optimal Subpattern Assignment,OSPA)距离显著下降,多目标的状态和数目的跟踪精度有所提升。在系统通信负担和单节点计算负担大幅降低的情况下,可达到与集中式融合处理相当的跟踪精度。

基于SCKF的GM-δ-GLMB多目标跟踪算法

多目标跟踪场景中,目标状态和量测均为随机分布。以高斯混合δ广义标签多目标多伯努利分布(gaussian mixtureδ-generalized labeled multi-bernoulli filter,GM-δ-GLMB)为代表的多目标跟踪方法,将状态和量测使用多目标多伯努利分量表示,通过多伯努利分量递推,实现对目标的跟踪与估计。GM-δ-GLMB在非线性多目标跟踪场景下会出现跟踪性能下降的问题。针对这一问题,将均方根容积卡尔曼与GM-δ-GLMB相结合,提高了GM-δ-GLMB算法在非线性场景的跟踪精度。同时,为减少运算复杂度和杂波对估计结果的影响,采用统计门限和最大似然概率策略,获取候选量测,用于多目标分量的更新。仿真结果表明,所提出的方法在非线性跟踪场景下具有良好的估计精度。

基于GGIW-PMB的衍生扩展目标跟踪

针对标准的扩展目标泊松多伯努利(Poisson multi-Bernoulli,PMB)滤波器难以有效跟踪衍生目标的问题,提出一种改进的PMB跟踪算法.算法采用随机矩阵法对扩展目标外形和尺寸建模,在滤波预测阶段利用多假设模型对衍生事件进行预测,得到多个伽玛高斯逆威沙特(gamma Gaussian inverse Wishart,GGIW)预测假设分量,最后在滤波更新阶段对预测分量更新得到扩展目标的运动状态和扩展形状估计.仿真结果表明,与标准的PMB滤波算法相比,所提算法有效改善衍生扩展目标的跟踪性能.

检测优化的标签多伯努利视频多目标跟踪算法

在检测器与跟踪器结合的视频多目标跟踪算法中,检测器好坏将直接影响整个跟踪算法性能,尤其是检测器的漏检以及误检,会导致目标的漏跟以及误跟,增加碎片化轨迹以及身份标签变换次数增加的问题。针对这些问题,在标签多伯努利的滤波框架下,设计了新的量测驱动新生目标识别方法以更快速精准地捕获新生目标。设计了目标重识别方法,结合标签多伯努利算法能够在短时间内维持标签的不变性,减少了碎片化轨迹及标签跳变数。引入新的模板选取策略,以避免将被遮挡的目标加入到模板中污染模板。考虑到标签多伯努利滤波为在线推理算法,采用了并行化加快算法的运算效率。结果表明,在标签多伯努利的框架下,提出算法能够有效解决标签跳变以及目标被遮挡无法准确跟踪的问题,在具有挑战性的MOT17数据集上进行测试,与其他相关滤波方法进行比较,具有不错的跟踪效果。