Computationally Eff&lt;roman&gt;i&lt;/roman&gt;cient Multi-Agent Multi-Object Tracking With Labeled Random Finite Sets

Li, Suqi; Battistelli, Giorgio; Chisci, Luigi; Yi, Wei; Wang, Bailu; Kong, Lingjiang

doi:10.1109/tsp.2018.2880704

Cited by 75 publications

(51 citation statements)

References 35 publications

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“…where 1 I1 ( ) = 1 if ∈ I 1 ; otherwise, 1 I1 ( ) = 0. It is remarked that the first-order moment matching approximation adopted here is a widely used approximation technique in the RFS based multi-object tracking algorithms [15], [35], [36].…”

Section: A Gci Fusion Of Mb Distributionsmentioning

confidence: 99%

“…The detailed proof of Proposition 1 is given in Appendix A. The expression (36) indicates that • The fused GMB densities of different clusters in C I are mutually independent [11] after discarding all the intercluster hypotheses (see [11, chap. 11, pg.…”

Section: Independence Approximation Of the Fused Gmb Densitymentioning

confidence: 99%

“…Remark 6. The L 1 -error between (11) and (36) can also be viewed as a measure of statistical dependence between the factorized GMB densities of different clusters. The smaller this L 1 -error is, the weaker the dependence between these factorized densities will be.…”

Section: Approximation Errormentioning

confidence: 99%

“…The combination of the independence approximation (36) and Proposition 4 enables a computationally efficient GCI-MB fusion algorithm. The fast clustering algorithm is employed at an early stage to form the LIC and thus obtain the approximated density.…”

Section: F Summarymentioning

confidence: 99%

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Computationally Efficient Distributed Multi-Sensor Fusion With Multi-Bernoulli Filter

Wang

et al. 2020

IEEE Trans. Signal Process.

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This paper proposes a computationally efficient algorithm for distributed fusion in a sensor network in which multi-Bernoulli (MB) filters are locally running in every sensor node for multi-target tracking. The generalized Covariance Intersection (GCI) fusion rule is employed to fuse multiple MB random finite set densities. The fused density comprises a set of fusion hypotheses that grow exponentially with the number of Bernoulli components. Thus, GCI fusion with MB filters can become computationally intractable in practical applications that involve tracking of even a moderate number of objects. In order to accelerate the multi-sensor fusion procedure, we derive a theoretically sound approximation to the fused density. The number of fusion hypotheses in the resulting density is significantly smaller than the original fused density. It also has a parallelizable structure that allows multiple clusters of Bernoulli components to be fused independently. By carefully clustering Bernoulli components into isolated clusters using the GCI divergence as the distance metric, we propose an alternative to build exactly the approximated density without exhaustively computing all the fusion hypotheses. The combination of the proposed approximation technique and the fast clustering algorithm can enable a novel and fast GCI-MB fusion implementation. Our analysis shows that the proposed fusion method can dramatically reduce the computational and memory requirements with small bounded L1-error. The Gaussian mixture implementation of the proposed method is also presented. In various numerical experiments, including a challenging scenario with up to forty objects, the efficacy of the proposed fusion method is demonstrated. arXiv:1906.07991v1 [eess.SY]

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Section: A Gci Fusion Of Mb Distributionsmentioning

confidence: 99%

Section: Independence Approximation Of the Fused Gmb Densitymentioning

confidence: 99%

Section: Approximation Errormentioning

confidence: 99%

Section: F Summarymentioning

confidence: 99%

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Computationally Efficient Distributed Multi-Sensor Fusion With Multi-Bernoulli Filter

Wang

et al. 2020

IEEE Trans. Signal Process.

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“…In [35] it has been proved that the resulting WKLA multiagent fusion, also known as Generalized Covariance Intersection (GCI), turns out to be immune to double counting of information and is, therefore, resilient to the presence of loops in the sensor network. The minimum cost associated to the WKLA-fused density is known in the literature as GCI divergence [13], [14]. The GCI divergence provides a sensible measure of the degree of dissimilarity among the set of local posteriors (see [13]), and can therefore be minimized with respect to the unknown drift and/or orientation parameters for sensor registration purposes.…”

Section: Introductionmentioning

confidence: 99%

Distributed Joint Sensor Registration and Multitarget Tracking via Sensor Network

Gao

Battistelli

Chisci

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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This paper addresses distributed registration of a sensor network for multitarget tracking. Each sensor gets measurements of the target position in a local coordinate frame, having no knowledge about the relative positions (referred to as drift parameters) and azimuths (referred to as orientation parameters) of its neighboring nodes. The multitarget set is modeled as an independent and identically distributed (i.i.d.) cluster random finite set (RFS), and a consensus cardinality probability hypothesis density (CPHD) filter is run over the network to recursively compute in each node the posterior RFS density. Then a suitable cost function, expressing the discrepancy between the local posteriors in terms of averaged Kullback-Leibler divergence, is minimized with respect to the drift and orientation parameters for sensor registration purposes. In this way, a computationally feasible optimization approach for joint sensor registraton and multitarget tracking is devised. Finally, the effectiveness of the proposed approach is demonstrated through simulation experiments on both tree networks and networks with cycles, as well as with both linear and nonlinear sensors.

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Tracking Multiple Underwater Vessels With Passive Sonar Using Beamforming and a Trajectory PHD Filter

Yi,

García‐Fernández,

Zhang

2023

Noisy Oceans

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Computationally Eff<roman>i</roman>cient Multi-Agent Multi-Object Tracking With Labeled Random Finite Sets

Cited by 75 publications

References 35 publications

Computationally Efficient Distributed Multi-Sensor Fusion With Multi-Bernoulli Filter

Computationally Efficient Distributed Multi-Sensor Fusion With Multi-Bernoulli Filter

Distributed Joint Sensor Registration and Multitarget Tracking via Sensor Network

Tracking Multiple Underwater Vessels With Passive Sonar Using Beamforming and a Trajectory PHD Filter

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