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Review:Recent advances in multisensor multitarge11racking using random finite set

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Frontiers of Information Technology & Electronic Engineering 2021年第1期22卷 5-24页

作者： Kai DA Tiancheng LI Yongfeng ZHU Hongqi FAN Qiang FU The National Key Laboratory of Science and Technology on ATR Nalional Universily of Defense TechnologyChangsha 410073China MOE Key Laboratory of Information Fusion Technology School of AutomationNorthwestern Polytechnical UniversityXi'an 710072China

In this study,we provide an overview of recent advances in multisensor multitarget tracking based on the random finite set(RFS)approach.The fusion that plays a fundamental role in multisensor filtering is classified into data-level multitarget measurement fusion and estimate-level multitarget density fusion,which share and fuse local measurements and posterior densities between sensors,respectively.Important properties of each fusion rule including the optimality and sub-optimality are presented.In particulax,two robust multitarget density-averaging approaches,arithmetic-and geometric-average fusion,are addressed in detail for various RFSs.Relevant research topics and remaining challenges are highlighted.

关键词： Multitarget tracking Multisensor fusion Average fusion random finite set Optimal fusion

Adaptive Multi-Bernoulli Filter Without Need of Prior Birth Multi-Bernoulli random finite set

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Chinese Journal of Electronics 2018年第1期27卷 115-122页

作者： YUAN Changshun WANG Jun LEI Peng SUN Jinping School of Electronic and Information Engineering Beihang University

Conventional Multi-Bernoulli(MBer) filter assumes that the birth MBer random finite set(RFS)is known a priori. However, this is not true for practical scenario. This paper proposes a novel extension of the MBer filter which eliminates the reliance of the prior birth MBer RFS and relaxes the limitation in new-born target appearance volume. The proposed filter classifies the measurements into survival measurements and birth measurements, and adaptively generates the birth MBer RFS using the birth measurements. The novel filtering equations that distinguish the persistent and new-born targets are derived. A Sequential Monte-Carlo(SMC) implementation of the proposed filter is given. Simulations are performed to verify the improvement in the performance of the proposed filter.

关键词： random finite set Adaptive multi-Bernoulli filter Sequential Monte-Carlo Multi-target tracking

PTMOT: A Probabilistic Multiple Object Tracker Enhanced by Tracklet Confidence for Autonomous Driving

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Automotive Innovation 2022年第3期5卷 260-271页

作者： Kun Jiang Yining Shi Taohua Zhou Mengmeng Yang Diange Yang State Key Lab of Automotive Safety and Energy Center for Intelligent Connected Vehicles and TransportationSchool of Vehicle and MobilityTsinghua UniversityLee Shau Kee Building No A639Beijing 100084China

Real driving scenarios,due to occlusions and disturbances,provide disordered and noisy measurements,which makes the task of multi-object tracking quite challenging.Conventional approach is to find deterministic data association;however,it has unstable performance in high clutter density.This paper proposes a novel probabilistic tracklet-enhanced multiple object tracker(PTMOT),which integrates Poisson multi-Bernoulli mixture(PMBM)filter with confidence of tracklets.The proposed method is able to realize efficient and robust probabilistic association for 3D multi-object tracking(MOT)and improve the PMBM filter’s continuity by smoothing single target hypothesis with global hypothesis.It consists of two key parts.First,the PMBM tracker based on sets of tracklets is implemented to realize probabilistic fusion of disordered measure-ments.Second,the confidence of tracklets is smoothed through a smoothing-while-filtering approach.Extensive MOT tests on nuScenes tracking dataset demonstrate that the proposed method achieves superior performance in different modalities.

关键词： 3D multi-object tracking random finite set Probabilistic association Tracklet confidence smoothing

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

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Chinese Journal of Aeronautics 2023年第2期36卷 179-190页

作者： Zhenzhen SU Hongbing JI Cong TIAN Yongquan ZHANG School of Computer Science and Technology Xidian UniversityXi'an 710071China School of Electronic Engineering Xidian UniversityXi'an 710071China

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.

关键词： Arithmetic average fusion Kullback-Leibler divergence Poisson multi-Bernoulli filter random finite set Target tracking

Track-Oriented Marginal Poisson Multi-Bernoulli Mixture Filter for Extended Target Tracking

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Chinese Journal of Electronics 2023年第5期32卷 1106-1119页

作者： DU Haocui XIE Weixin LIU Zongxiang LI Liangqun ATR Key Laboratory Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

In this paper, we derive and propose a track-oriented marginal Poisson multi-Bernoulli mixture(TO-MPMBM) filter to address the problem that the standard random finite set filters cannot build continuous trajectories for multiple extended targets. First, the Poisson point process model and the multi-Bernoulli mixture(MBM) model are used to establish the set of birth trajectories and the set of existing trajectories, respectively.Second, the proposed filter recursively propagates the marginal association distributions and the Poisson multiBernoulli mixture(PMBM) density over the set of alive trajectories. Finally, after pruning and merging process,the trajectories with existence probability greater than the given threshold are extracted as the estimated target trajectories. A comparison of the proposed filter with the existing trajectory filters in two classical scenarios confirms the validity and reliability of the TO-MPMBM filter.

关键词： Extended target tracking random finite set Poisson multi-Bernoulli mixture Poisson point process Marginal distribution Target trajectory

IMM Robust Cardinality Balance Multi-Bernoulli Filter for Multiple Maneuvering Target Tracking with Interval Measurement

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Chinese Journal of Electronics 2021年第6期30卷 1141-1151页

作者： YANG Biao ZHU Shengqi HE Xiongpeng National Laboratory of Radar Signal Processing Xidian University

This paper presents a novel Interacting multi-model(IMM) Robust Cardinality balance multitarget multi-Bernoulli(R-CBMe MBer) filter to solve the maneuvering target tracking problem in the case of interval measurement, unknown detection probability and unknown clutter density. In essence, IMM R-CBMe MBer filter is an extended application of R-CBMe MBer filter.In the IMM R-CBMe MBer filter, the target state is first extended to distinguish clutter from the real target.The detection probability and model probability of the target can be adaptively updated. Then, generalized likelihood function and IMM algorithm are introduced to interactively predict and update the state of the target in the IMM R-CBMe MBer filtering process. In addition,a particle application of the IMM R-CBMe MBer filter is given, and a numerical experiment is designed under nonlinear conditions. Meanwhile, Doppler information of the target is employed to estimate the velocity of each maneuvering target. Numerical experiments also verify that the IMM R-CBMe MBer filter can effectively estimate the target position, target velocity, target detection probability and clutter number in the condition of unknown detection probability, unknown clutter rate and interval measurement.

关键词： Multiple maneuvering target tracking random finite set Interacting multiple model Cardinality balance multiple targets multi-Bernoulli filter Generalized likelihood function

A Poisson multi-Bernoulli mixture filter with spawning based on Kullback–Leibler divergence minimization

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Chinese Journal of Aeronautics 2021年第11期34卷 154-168页

作者： Zhenzhen SU Hongbing JI Yongquan ZHANG School of Electronic Engineering Xidian UniversityXi’an 710071China

In the classical form,the Poisson Multi-Bernoulli Mixture(PMBM)filter uses a PMBM density to describe target birth,surviving,and death,which does not model the appearance of spawned targets.Although such a model can handle target birth,surviving,and death well,its performance may degrade when target spawning arises.The reason for this is that the original PMBM filter treats the spawned targets as birth targets,ignoring the surviving targets’information.In this paper,we propose a Kullback–Leibler Divergence(KLD)minimization based derivation for the PMBM prediction step,including target spawning,in which the spawned targets are modeled using a Poisson Point Process(PPP).Furthermore,to improve the computational efficiency,three approximations are used to implement the proposed algorithm,such as the Variational MultiBernoulli(VMB)filter,the Measurement-Oriented marginal MeMBer/Poisson(MOMB/P)filter,and the Track-Oriented marginal MeMBer/Poisson(TOMB/P)filter.Finally,simulation results demonstrate the validity of the proposed filter by using the spawning model in these three approximations.

关键词： Kullback–Leibler Divergence Multi-target tracking PMBM filter Poisson Point Process random finite set Target spawning

Multi-Bernoulli Filter for Tracking Multiple Targets Using Sensor Array

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China Ocean Engineering 2020年第2期34卷 245-256页

作者： ZHANG Guang-pu ZHENG Ce QIU Long-hao SUN Si-bo Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001China College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China Qindao Haina Underwater Information Technology Co. Ltd.Qindao 266500China

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.

关键词： multiple target tracking multi-Bernoulli filter direction of arrival estimation random finite set track-before-detect

On the sensor order in sequential integrated probability data association filter

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Science China(Information Sciences) 2012年第3期55卷 491-500页

作者： WANG Yang1 , JING ZhongLiang1, HU ShiQiang1, ZHANG HongJian2 & WU JingJing1 1School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China 2School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

The processing order of sensors with different detection probabilities and in different clutter densities in a multi-sensor system is investigated in this paper. A sequential implementation of the integrated probability data association (IPDA) algorithm under random set framework is derived. Under the assumptions of different detection probabilities and different clutter densities of individual sensor in a multi-sensor system, we reach the conclusion that the sequential IPDA filter depends on the order analyzing the target existence probability of varying sensor orders. Moreover, we obtain the optimal order of sensors for the sequential IPDA filter in terms of maximizing the target existence probability. The conclusions are demonstrated by simulation results.

关键词： sequential integrated probability data association random finite set optimal sensor order