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Consensus-based distributed algorithm for multisensor-multitarget tracking under unknown-but-bounded disturbances. / Amelina, Natalia; Erofeeva, Victoria; Granichin, Oleg; Ivanskiy, Yury; Jiang, Yuming; Proskurnikov, Anton; Sergeenko, Anna.

In: IFAC-PapersOnLine, Vol. 53, 2020, p. 3589-3595.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Amelina, N, Erofeeva, V, Granichin, O, Ivanskiy, Y, Jiang, Y, Proskurnikov, A & Sergeenko, A 2020, 'Consensus-based distributed algorithm for multisensor-multitarget tracking under unknown-but-bounded disturbances', IFAC-PapersOnLine, vol. 53, pp. 3589-3595. https://doi.org/10.1016/j.ifacol.2020.12.1756

APA

Amelina, N., Erofeeva, V., Granichin, O., Ivanskiy, Y., Jiang, Y., Proskurnikov, A., & Sergeenko, A. (2020). Consensus-based distributed algorithm for multisensor-multitarget tracking under unknown-but-bounded disturbances. IFAC-PapersOnLine, 53, 3589-3595. https://doi.org/10.1016/j.ifacol.2020.12.1756

Vancouver

Amelina N, Erofeeva V, Granichin O, Ivanskiy Y, Jiang Y, Proskurnikov A et al. Consensus-based distributed algorithm for multisensor-multitarget tracking under unknown-but-bounded disturbances. IFAC-PapersOnLine. 2020;53:3589-3595. https://doi.org/10.1016/j.ifacol.2020.12.1756

Author

Amelina, Natalia ; Erofeeva, Victoria ; Granichin, Oleg ; Ivanskiy, Yury ; Jiang, Yuming ; Proskurnikov, Anton ; Sergeenko, Anna. / Consensus-based distributed algorithm for multisensor-multitarget tracking under unknown-but-bounded disturbances. In: IFAC-PapersOnLine. 2020 ; Vol. 53. pp. 3589-3595.

BibTeX

@article{d717e7fc559549dc9be7c53fd9d44b6e,
title = "Consensus-based distributed algorithm for multisensor-multitarget tracking under unknown-but-bounded disturbances",
abstract = "We consider a dynamic network of sensors that cooperate to estimate parameters of multiple targets. Each sensor can observe parameters of a few targets, reconstructing the trajectories of the remaining targets via interactions with “neighbouring” sensors. The multitarget tracking has to be provided in the face of uncertainties, which include unknown-but-bounded drift of parameters, noise in observations and distortions introduced by communication channels. To provide tracking in presence of these uncertainties, we employ a distributed algorithm, being an “offspring” of a consensus protocol and the stochastic gradient descent. The mathematical results on the algorithm's convergence are illustrated by numerical simulations.",
keywords = "Consensus, Multitarget tracking, Randomized algorithms, Sensor network",
author = "Natalia Amelina and Victoria Erofeeva and Oleg Granichin and Yury Ivanskiy and Yuming Jiang and Anton Proskurnikov and Anna Sergeenko",
note = "Publisher Copyright: {\textcopyright} 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0) Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 21st IFAC World Congress 2020 ; Conference date: 12-07-2020 Through 17-07-2020",
year = "2020",
doi = "10.1016/j.ifacol.2020.12.1756",
language = "English",
volume = "53",
pages = "3589--3595",
journal = "IFAC-PapersOnLine",
issn = "2405-8971",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Consensus-based distributed algorithm for multisensor-multitarget tracking under unknown-but-bounded disturbances

AU - Amelina, Natalia

AU - Erofeeva, Victoria

AU - Granichin, Oleg

AU - Ivanskiy, Yury

AU - Jiang, Yuming

AU - Proskurnikov, Anton

AU - Sergeenko, Anna

N1 - Publisher Copyright: © 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0) Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - We consider a dynamic network of sensors that cooperate to estimate parameters of multiple targets. Each sensor can observe parameters of a few targets, reconstructing the trajectories of the remaining targets via interactions with “neighbouring” sensors. The multitarget tracking has to be provided in the face of uncertainties, which include unknown-but-bounded drift of parameters, noise in observations and distortions introduced by communication channels. To provide tracking in presence of these uncertainties, we employ a distributed algorithm, being an “offspring” of a consensus protocol and the stochastic gradient descent. The mathematical results on the algorithm's convergence are illustrated by numerical simulations.

AB - We consider a dynamic network of sensors that cooperate to estimate parameters of multiple targets. Each sensor can observe parameters of a few targets, reconstructing the trajectories of the remaining targets via interactions with “neighbouring” sensors. The multitarget tracking has to be provided in the face of uncertainties, which include unknown-but-bounded drift of parameters, noise in observations and distortions introduced by communication channels. To provide tracking in presence of these uncertainties, we employ a distributed algorithm, being an “offspring” of a consensus protocol and the stochastic gradient descent. The mathematical results on the algorithm's convergence are illustrated by numerical simulations.

KW - Consensus

KW - Multitarget tracking

KW - Randomized algorithms

KW - Sensor network

UR - http://www.scopus.com/inward/record.url?scp=85099879494&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2020.12.1756

DO - 10.1016/j.ifacol.2020.12.1756

M3 - Conference article

AN - SCOPUS:85099879494

VL - 53

SP - 3589

EP - 3595

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8971

T2 - 21st IFAC World Congress 2020

Y2 - 12 July 2020 through 17 July 2020

ER -

ID: 78863848