Consensus‐based multiple‐model Bayesian filtering for distributed tracking

Battistelli, Giorgio; Chisci, Luigi; Fantacci, Claudio; Farina, A.; Graziano, A.

doi:10.1049/iet-rsn.2014.0071

Cited by 26 publications

(12 citation statements)

References 26 publications

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“…The resulting D-JDTC-BF algorithm is summarized in Table 2. [32] on distributed multiple-model Bayesian tracking of a maneuvering target. In fact, D-JDTC-BF allows to perform also target detection and classification, besides tracking, and considers the presence of clutter as well as target appearance/disappearance.…”

Section: Distributed Jdtc-bf Algorithmmentioning

confidence: 99%

Multi-sensor joint target detection, tracking and classification via Bernoulli filter

Li,

Wei,

Battistelli

et al. 2021

Preprint

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This paper focuses on joint detection, tracking and classification (JDTC) of a target via multi-sensor fusion. The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes. Accordingly, it is modeled as a suitably extended Bernoulli random finite set (RFS) uniquely characterized by existence, classification, class-conditioned mode and class&mode-conditioned state probability distributions. By designing suitable centralized and distributed rules for fusing information on target existence, class, mode and state from different sensor nodes, novel centralized and distributed JDTC Bernoulli filters (C-JDTC-BF and D-JDTC-BF), are proposed. The performance of the proposed JDTC-BF approach is evaluated by means of simulation experiments.

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Section: Distributed Jdtc-bf Algorithmmentioning

confidence: 99%

Multi-sensor joint target detection, tracking and classification via Bernoulli filter

Li,

Wei,

Battistelli

et al. 2021

Preprint

Self Cite

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“…It was originally introduced in [25] for a distributed state estimation problem; later, a mathematical rigorous treatment of it was detailed in [24], where CI is interpreted as a consensus on probability density functions in the Kullback-Leibler average sense. Following the same consensus paradigm, [10] presented a novel consensus cardinalized probability hypothesis density filter to study the distributed multitarget tracking problem over a sensor network, and [80] designed a consensus-based multiplemodel Bayesian filter for the distributed tracking task of a maneuvering target. More recently, [81] applied CI to design the distributed unscented Kalman filters for systems with state saturations and sensor saturations.…”

Section: Consensus On Informationmentioning

confidence: 99%

A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Wang

Wei

et al. 2015

Discrete Dynamics in Nature and Society

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Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development of multisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.

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“…In recent years, there has been a surge of interest in distributed state estimation of non-linear dynamical systems. Applications include economic dispatch and wide area monitoring in power systems [1] and target tracking in wireless sensor networks (WSNs) [2][3][4][5][6][7]. There are several reasons to prefer distributed state estimation techniques over centralised state estimation in WSNs.…”

Section: Introductionmentioning

confidence: 99%

“…Among distributed estimation techniques, consensus-based distributed implementations of estimators have received great attention, in recent years [2][3][4][5][6][7]. In consensus-based methods, sensor nodes communicate only with their neighbouring nodes to agree upon a sum, maximum, average, or other certain quantities of interest [9].…”

Section: Introductionmentioning

confidence: 99%

Adaptive consensus‐based distributed state estimator for non‐linear systems in the presence of multiplicative noise

Keshavarz-Mohammadiyan

Khaloozadeh

2017

IET signal process.

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The problem of consensus-based distributed state estimation of a non-linear dynamical system in the presence of multiplicative observation noise is investigated in this study. Generalised extended information filter (GEIF) is developed for nonlinear state estimation in the information-space framework. To fuse the information contribution of local estimators, an average consensus algorithm is employed. To achieve faster convergence towards consensus, a novel technique is proposed to modify the consensus weights, adaptively. Computational complexity of the proposed estimator is also analysed theoretically to demonstrate the computational advantage of the adaptive consensus-based distributed GEIF over the centralised counterpart. Moreover, stability of local estimators in terms of mean-square boundedness of state estimation error is guaranteed, in the presence of multiplicative noise. Simulation results are provided to evaluate performance of the proposed adaptive distributed estimator for a target-tracking problem in a wireless sensor network.

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Consensus‐based multiple‐model Bayesian filtering for distributed tracking

Cited by 26 publications

References 26 publications

Multi-sensor joint target detection, tracking and classification via Bernoulli filter

Multi-sensor joint target detection, tracking and classification via Bernoulli filter

A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Adaptive consensus‐based distributed state estimator for non‐linear systems in the presence of multiplicative noise

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