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SENSOR NETWORK CONTROL BASED ON RANDOMIZED AND MULTI-AGENT APPROACHES. / Sergeenko, Anna; Granichin, Oleg.

In: Cybernetics and Physics, Vol. 11, No. 2, 30.09.2022, p. 94-105.

Research output: Contribution to journal › Article › peer-review

Harvard

Sergeenko, A & Granichin, O 2022, 'SENSOR NETWORK CONTROL BASED ON RANDOMIZED AND MULTI-AGENT APPROACHES', Cybernetics and Physics, vol. 11, no. 2, pp. 94-105. https://doi.org/10.35470/2226-4116-2022-11-2-94-105

APA

Sergeenko, A., & Granichin, O. (2022). SENSOR NETWORK CONTROL BASED ON RANDOMIZED AND MULTI-AGENT APPROACHES. Cybernetics and Physics, 11(2), 94-105. https://doi.org/10.35470/2226-4116-2022-11-2-94-105

Vancouver

Sergeenko A, Granichin O. SENSOR NETWORK CONTROL BASED ON RANDOMIZED AND MULTI-AGENT APPROACHES. Cybernetics and Physics. 2022 Sep 30;11(2):94-105. https://doi.org/10.35470/2226-4116-2022-11-2-94-105

Author

Sergeenko, Anna ; Granichin, Oleg. / SENSOR NETWORK CONTROL BASED ON RANDOMIZED AND MULTI-AGENT APPROACHES. In: Cybernetics and Physics. 2022 ; Vol. 11, No. 2. pp. 94-105.

BibTeX

@article{aa553f32cc964be4823f61d84520521c,
title = "SENSOR NETWORK CONTROL BASED ON RANDOMIZED AND MULTI-AGENT APPROACHES",
abstract = "In this paper, a development of randomized and multi-agent algorithms is presented. The examples and their advantages are discussed. Different combined algo-rithms, which are applicable for the multi-sensor multi-target tracking problem are shown. These algorithms be-long to the class of methods used in derivative-free optimization and has proven efficacy in the problems includ-ing significant non-statistical uncertainties. The new al-gorithm, which is an Accelerated consensus-based SPSA algorithm is validated through the simulation.The main feature of that algorithm, combining the SPSA tech-niques, iterative averaging (“Local Voting Protocol”) and Nesterov Acceleration Method, is the ability to solve distributed optimization problems in the presence of signals with fully uncertain distribution; the only assumption is the signal{\textquoteright}s limitation.",
keywords = "Local Voting Protocol, multi-agent algorithms, Nesterov Acceleration, Randomized algorithms, SPSA, Tracking",
author = "Anna Sergeenko and Oleg Granichin",
note = "Publisher Copyright: {\textcopyright} 2022, Institute for Problems in Mechanical Engineering, Russian Academy of Sciences. All rights reserved.",
year = "2022",
month = sep,
day = "30",
doi = "10.35470/2226-4116-2022-11-2-94-105",
language = "English",
volume = "11",
pages = "94--105",
journal = "Cybernetics and Physics",
issn = "2223-7038",
publisher = "IPACS",
number = "2",

}

RIS

TY - JOUR

T1 - SENSOR NETWORK CONTROL BASED ON RANDOMIZED AND MULTI-AGENT APPROACHES

AU - Sergeenko, Anna

AU - Granichin, Oleg

N1 - Publisher Copyright: © 2022, Institute for Problems in Mechanical Engineering, Russian Academy of Sciences. All rights reserved.

PY - 2022/9/30

Y1 - 2022/9/30

N2 - In this paper, a development of randomized and multi-agent algorithms is presented. The examples and their advantages are discussed. Different combined algo-rithms, which are applicable for the multi-sensor multi-target tracking problem are shown. These algorithms be-long to the class of methods used in derivative-free optimization and has proven efficacy in the problems includ-ing significant non-statistical uncertainties. The new al-gorithm, which is an Accelerated consensus-based SPSA algorithm is validated through the simulation.The main feature of that algorithm, combining the SPSA tech-niques, iterative averaging (“Local Voting Protocol”) and Nesterov Acceleration Method, is the ability to solve distributed optimization problems in the presence of signals with fully uncertain distribution; the only assumption is the signal’s limitation.

AB - In this paper, a development of randomized and multi-agent algorithms is presented. The examples and their advantages are discussed. Different combined algo-rithms, which are applicable for the multi-sensor multi-target tracking problem are shown. These algorithms be-long to the class of methods used in derivative-free optimization and has proven efficacy in the problems includ-ing significant non-statistical uncertainties. The new al-gorithm, which is an Accelerated consensus-based SPSA algorithm is validated through the simulation.The main feature of that algorithm, combining the SPSA tech-niques, iterative averaging (“Local Voting Protocol”) and Nesterov Acceleration Method, is the ability to solve distributed optimization problems in the presence of signals with fully uncertain distribution; the only assumption is the signal’s limitation.

KW - Local Voting Protocol

KW - multi-agent algorithms

KW - Nesterov Acceleration

KW - Randomized algorithms

KW - SPSA

KW - Tracking

UR - http://www.scopus.com/inward/record.url?scp=85139185264&partnerID=8YFLogxK

U2 - 10.35470/2226-4116-2022-11-2-94-105

DO - 10.35470/2226-4116-2022-11-2-94-105

M3 - Article

AN - SCOPUS:85139185264

VL - 11

SP - 94

EP - 105

JO - Cybernetics and Physics

JF - Cybernetics and Physics

SN - 2223-7038

IS - 2

ER -

ID: 100545068