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distributed multi-target tracking with low information updates via an integral-type event-based approach

SIGNAL PROCESSING 2024年 214卷

作者： Shen, Kai Zhang, Chengxi Dong, Peng Jing, Zhongliang Leung, Henry Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai 200240 Peoples R China Univ Calgary Dept Elect & Comp Engn Calgary AB T2N IN4 Canada

The existing event-triggered (ET) strategies for distributed multi-target filter commonly employ triggering conditions focusing on the instantaneous information discrepancy. Although this approach can save com-munication resource, it suffers from its sensitivity to instantaneous disturbance and outliers that results in unnecessary communications. In this correspondence, an integral-type event-triggered mechanism is integrated with the consensus-based labeled multi-Bernoulli filter (LMB). The proposed algorithm provides a passivation effect that lowers the communication activities while preserving the tracking performance in the uncertain environment with disturbance. The theoretical analysis demonstrates that the integral-type event-triggered mechanism introduces bounded information discrepancy in the sense of Kullback-Leibler (KL) divergence. The proposed algorithm is also verified by numerical simulations in a target tracking scenario.

关键词： Consensus-based algorithm Labeled multi-Bernoulli filter distributed multi-target tracking Event-triggered Integral-type

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Arithmetic Average Based multi-sensor TPHD Filter for distributed multi-target tracking

Arithmetic Average Based Multi-sensor TPHD Filter for Distri...

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IEEE International Conference on multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Fu, Jiazheng Chai, Lei Zhang, Boxiang Yi, Wei Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

Compared with the probability hypothesis density (PHD) filter for sets of targets, the trajectory probability hypothesis density (TPHD) filter can estimate the sets of trajectories in a principle way and has better target tracking performance. This paper aims at extending the TPHD filter to distributed multi-target tracking (MTT) for the multi-sensor system. However, in the trajectory set based distributed fusion implementation, the trajectory state difference phenomenon makes the clustering and merging techniques unfeasible in trajectory state space. To address this problem, this paper studies the space decomposition of the TPHD and proposes a distributed MTT method based on the TPHD filter with the weighted arithmetic average (WAA) fusion rule. First, we prove the rationality of the space decomposition in the posterior density of the TPHD filter. Then, based on the proposed property, we derive the WAA fusion formulation of the TPHD filter by minimizing the weighted sum of Kullback-Leibler divergences (KLD) from local posterior densities, and develop the analytical Gaussian mixture (GM) implementation with the L-scan approximation. Numerical results demonstrate the efficacy of the proposed fusion method.

关键词： distributed multi-target tracking trajectory probability hypothesis density filter information fusion weighted arithmetic average

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Resource Scheduling for distributed multi-target tracking in Netted Colocated MIMO Radar Systems

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年第0期68卷 1602-1617页

作者： Yi, Wei Yuan, Ye Hoseinnezhad, Rua Kong, Lingjiang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China RMIT Univ Sch Engn Carlton Vic 3083 Australia

In this paper, an effective solution is proposed for joint beam and power scheduling (JBPS) in the netted Colocated MIMO (C-MIMO) radar systems for distributed multi-target tracking (MTT). At its core, the proposed solution includes a distributed fusion architecture that reduces the communication requirements while maintaining the overall robustness of the system. The distributed fusion architecture employs the covariance intersection (CI) fusion to address the unknown information correlations among radar nodes. Each C-MIMO radar node in the network can generate a time-varying number of beams with controllable transmitting power by waveform synthesis, thus is capable of accomplishing multiple tracking tasks simultaneously. To maximize the global MTT performance of the radar network, the proposed JBPS solution implements an online resource scheduling, regarding both the generated beams and the transmitted power of all radar nodes, based on the feedback of the MTT results. A scaled accuracy-based objective function is designed to quantify the global MTT performance while properly taking into account different target priorities on resource allocation. The Bayesian Cram & x00E9;r-Rao lower bound (BCRLB) for CI fusion rule is derived and utilized as the constituent of the objective function since it provides a lower bound on the accuracy of the target state estimates. As the formulated JBPS problem is non-convex, we propose a fast reward-based iterative descending approach to solve it effectively. Numerical results show that the proposed JBPS can deliver superior performance in terms of maximizing the overall MTT performance while possessing high flexibility on the resource allocation regarding different target priorities.

关键词： Resource scheduling netted radar system distributed multi-target tracking covariance intersection BCRLB

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Resilient multi-Robot multi-target tracking

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第3期21卷 4311-4327页

作者： Ramachandran, Ragesh Kumar Fronda, Nicole Preiss, James A. Dai, Zhenghao Sukhatme, Gaurav S. Univ Southern Calif Dept Comp Sci Los Angeles CA 90089 USA Oregon State Univ Dept Elect & Comp Engn Corvallis OR 97331 USA CALTECH Dept Comp & Math Sci Pasadena CA 91125 USA Univ Southern Calif Dept Comp Sci Los Angeles CA 90089 USA

We address the problem of ensuring resource availability in a networked multi-robot system performing distributed target tracking. Specifically, we consider a multi-target tracking scenario where the targets are driven by exogenous inputs that are unknown to the robots performing the tracking task. Robots track the positions of targets using a form of the distributed Kalman Filter (DKF). We use the trace of each robot's sensor measurement noise covariance matrix as a measure of its sensing quality. When a robot's sensing quality deteriorates, the team's communication graph is modified by adding edges such that the robot with deteriorating sensor quality may share information with other robots to improve the team's target tracking ability. This computation is performed centrally and is designed to work without a large change in the number of active inter-robot communication links. Our method generates coordinates for the robots such the new communication graph can be realized in 3D. To achieve this, we propose two mixed integer semi-definite programming formulations, namely an 'agent-centric' strategy and a 'team-centric' strategy. We implement both formulations and a greedy, baseline strategy in simulation. Our simulation results show that the team-centric approach outperforms both agent-centric and greedy methods. Additionally, we show the effectiveness of our method in real-world settings through a multi-robot experiment performed in real-time.

关键词： Robot sensing systems target tracking Robot kinematics Noise measurement Sensors Covariance matrices Resilience multirobot system resilience distributed multi-target tracking

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distributed Fusion of Labeled multi-Bernoulli Filters Based on Arithmetic Average

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 656-660页

作者： Wei, Jingxin Luo, Feng Qi, Jiawei Luo, Xianxian Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Hangzhou Inst Technol Hangzhou 311231 Peoples R China

This letter considers the distributed fusion for Labeled multi-Bernoulli (LMB) filters under the multi-sensor multi-target tracking scenario. In practice, a novel fusion method that combines the label-free strategy with the fusion method based on the Arithmetic Average (AA) is proposed. Firstly, the label-free version of the LMB posterior is obtained. Then, the corresponding Probability Hypothesis Density (PHD) is acquired and fused by the merging-based PHD-AA fusion method. Finally, the labels of the fused distributions are reassigned. The simulation shows that the distributed LMB filter using the proposed fusion method can achieve more accurate multi-target state estimates with relatively low computational burden under clutter and missed detection environment compared to other existing methods.

关键词： Radio frequency Merging Signal processing algorithms target tracking Partitioning algorithms Radar signal processing Mathematical models Arithmetic average fusion distributed multi-target tracking labeled multi-Bernoulli (LMB) filter random finite set

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multi-Agent Fusion With Different Limited Fields-of-View

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 1560-1575页

作者： Wang, Bailu Li, Suqi Battistelli, Giorgio Chisci, Luigi Yi, Wei Univ Firenze Dipartimento Ingn Informaz I-50139 Florence Italy Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

A key objective of multi-agent surveillance systems is to monitor a much larger region than the limited field-of-view (FoV) of any individual agent by successfully exploiting cooperation among multiple agents. Whenever either a centralized or a distributed approach is pursued, this goal cannot be achieved unless an appropriately designed fusion strategy is adopted. This paper presents a novel information fusion approach by considering for each agent a known limited, and possibly different, FoV. The proposed method, named Bayesian-operation InvaRiance on Difference-sets (BIRD) fusion, relies on Generalized Covariance Intersection (GCI) and exploits a general and exact decomposition of each multi-object posterior by partitioning the global FoV, i.e. the union of the FoVs of the fusing agents, into common and exclusive FoVs. It is shown how BIRD fusion can be used to perform multi-object estimation based on random finite sets on both a centralized and a distributed peer-to-peer sensor network. Simulation experiments on realistic multi-object tracking scenarios demonstrate the effectiveness of BIRD fusion.

关键词： Peer-to-peer computing Sensors Sensor fusion Birds Radio frequency Estimation Surveillance multi-agent fusion partially non-overlapping FoVs distributed multi-target tracking random finite set BIRD fusion

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Resilient labeled multi-Bernoulli fusion with peer-to-peer sensor network

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INFORMATION FUSION 2023年第1期100卷

作者： Gao, Lin Battistelli, Giorgio Chisci, Luigi Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Univ Firenze Dipartimento Ingn Informaz DINFO Florence Italy

This paper addresses fusion of labeled multi-Bernoulli (LMB) densities in a peer-to-peer sensor network subject to misbehaviors due to abnormal conditions of the signal propagation channel (i.e. low signal-noise-ratio) or to malicious attacks. In such situations unintentional or intentional modifications of the transmitted messages can occur which degrade the performance of standard fusion methods. In order to overcome the aforementioned difficulties, we propose a resilient approach to distributed fusion of LMB densities in which each node exploits the local density as trusted source of information to detect the modified/false data coming from neighboring nodes. The implementation issues of the proposed method as well as possible cyber-attack models in the context of multi-object filtering are also discussed. The performance of the proposed method is verified via simulation experiments concerning fusion under cyber-attacks.

关键词： distributed multi-target tracking multi-object fusion Attack-resilient estimation Random finite set Labeled multi-Bernoulli

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distributed multi-sensor fusion using generalised multi-Bernoulli densities

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IET RADAR SONAR AND NAVIGATION 2017年第3期11卷 434-443页

作者： Yi, Wei Jiang, Meng Hoseinnezhad, Reza Wang, Bailu Univ Elect Sci & Technol China Sch Elect Engn Chengdu Sichuan Peoples R China RMIT Univ Sch Engn Bundoora Vic 3083 Australia

A new method for distributed multi-target tracking with multistatic radar systems is presented. The proposed method is based on using generalised covariance intersection (GCI) of multi-object densities for fusion of the posteriors within a multiobject Bayesian filtering scheme. The presented solution is particularly formulated for sensor fusion with posterior densities that are parameterised as generalised multi-Bernoulli (GMB) distributions which are the unlabelled version of VoCVo densities by discarding the labels. To obtain a closed-form solution for fusing GMB densities, the authors use an efficient approximation to the densities. The approximated density is another GMB density that preserves both the first-order moment (intensity or PHD) and the cardinality distribution of the original density. As such, it is called the second-order approximation of the GMB (SO-GMB) density. The resulting explicit expressions for the GCI fusion using SO-GMB approximations allow distributed sensor fusion, not only with VoCVo filters, but also with M-generalised labelled multi-Bernoulli and labelled multi-Bernoulli filters being in place as local filters in the multistatic radar system. In two challenging multi-target tracking scenarios, the tracking performance of the proposed method is shown to outperform the state of art.

关键词： sensor fusion target tracking radar signal processing filtering theory radar tracking approximation theory distributed multisensor fusion generalised multi-Bernoulli densities distributed multi-target tracking multistatic radar system generalised covariance intersection multi-object Bayesian filtering scheme generalised multiBernoulli distributions first-order moment cardinality distribution second-order approximation SO-GMB approximations multi-Bernoulli filters

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