A Particle Multi-Target Tracker for Superpositional Measurements Using Labeled Random Finite Sets

Papi, Francesco; Kim, Du Yong

doi:10.1109/tsp.2015.2443727

Cited by 81 publications

(41 citation statements)

References 49 publications

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“…Measurement equation (58) models the amplitude of the received acoustic signal at a sensor from incoherently emitting targets [35] and is not superpositional so filters such as [17], [18] cannot be used.…”

Section: Numerical Simulationsmentioning

confidence: 99%

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Track-before-detect labeled multi-bernoulli particle filter with label switching

García-Fernández

2016

IEEE Trans. Aerosp. Electron. Syst.

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Abstract-This paper presents a multitarget tracking particle filter (PF) for general track-before-detect measurement models. The PF is presented in the random finite set framework and uses a labelled multi-Bernoulli approximation. We also present a label switching improvement algorithm based on Markov chain Monte Carlo that is expected to increase filter performance if targets get in close proximity for a sufficiently long time. The PF is tested in two challenging numerical examples.

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Section: Numerical Simulationsmentioning

confidence: 99%

“…There are multiple track-before-detect MTT algorithms that are not general as they require specific measurement models such as superpositional sensors [17], [18], models with likelihood factorisation over single targets [19], pixelised sensors [20]- [22] or the model of the histogram probabilistic multi-hypothesis tracker [23].…”

Section: Introductionmentioning

confidence: 99%

Track-before-detect labeled multi-bernoulli particle filter with label switching

García-Fernández

2016

IEEE Trans. Aerosp. Electron. Syst.

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“…Under the standard multi-object system model, the filtering recursion (7) admits an analytic solution known as the Generalized labeled Multi-Bernoulli (GLMB) filter [28], [30]. For a general system model, the generic multi-object particle filter can be applied, see for example [19].…”

Section: B Bayes Recursionmentioning

confidence: 99%

A Multi-Scan Labeled Random Finite Set Model for Multi-Object State Estimation

2019

IEEE Trans. Signal Process.

100

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State space models in which the system state is a finite set-called the multi-object state-have generated considerable interest in recent years. Smoothing for state space models provides better estimation performance than filtering by using the full posterior rather than the filtering density. In multiobject state estimation, the Bayes multi-object filtering recursion admits an analytic solution known as the Generalized Labeled Multi-Bernoulli (GLMB) filter. In this work, we extend the analytic GLMB recursion to propagate the multi-object posterior. We also propose an implementation of this so-called multi-scan GLMB posterior recursion using a similar approach to the GLMB filter implementation.

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“…More recently, the concept of labeled RFSs has been introduced to cope with a multi-target tracking problem, and its implementations include labeled multi-Bernoulli [7] and generalized labeled multi-Bernoulli [8], [9] approximations. These analytic approximations of a multi-target Bayes filter through an RFS and labeled RFSs have a various scope of applications including radar target tracking [10], [11], computer vision [12], [13], and sensor networks [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Target Birth Intensity Estimation Using Measurement-Driven PHD Filter

Zhang¹,

Ge²,

Yang³

2016

ETRI J

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The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target‐tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement‐driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre‐filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement‐driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

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A Particle Multi-Target Tracker for Superpositional Measurements Using Labeled Random Finite Sets

Cited by 81 publications

References 49 publications

Track-before-detect labeled multi-bernoulli particle filter with label switching

Track-before-detect labeled multi-bernoulli particle filter with label switching

A Multi-Scan Labeled Random Finite Set Model for Multi-Object State Estimation

Target Birth Intensity Estimation Using Measurement-Driven PHD Filter

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