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Distributed Tracking via Simultaneous Perturbation Stochastic Approximation-based Consensus Algorithm. / Erofeeva, Victoria; Granichin, Oleg; Amelina, Natalia; Ivanskiy, Yury; Jiang, Yuming.

2019 IEEE 58th Conference on Decision and Control, CDC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. стр. 6050-6055 9030129 (IEEE Conference on Decision and Control).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование

Harvard

Erofeeva, V, Granichin, O, Amelina, N, Ivanskiy, Y & Jiang, Y 2019, Distributed Tracking via Simultaneous Perturbation Stochastic Approximation-based Consensus Algorithm. в 2019 IEEE 58th Conference on Decision and Control, CDC 2019., 9030129, IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., стр. 6050-6055, 58th IEEE Conference on Decision and Control, CDC 2019, Nice, Франция, 11/12/19. https://doi.org/10.1109/CDC40024.2019.9030129

APA

Erofeeva, V., Granichin, O., Amelina, N., Ivanskiy, Y., & Jiang, Y. (2019). Distributed Tracking via Simultaneous Perturbation Stochastic Approximation-based Consensus Algorithm. в 2019 IEEE 58th Conference on Decision and Control, CDC 2019 (стр. 6050-6055). [9030129] (IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC40024.2019.9030129

Vancouver

Erofeeva V, Granichin O, Amelina N, Ivanskiy Y, Jiang Y. Distributed Tracking via Simultaneous Perturbation Stochastic Approximation-based Consensus Algorithm. в 2019 IEEE 58th Conference on Decision and Control, CDC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. стр. 6050-6055. 9030129. (IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC40024.2019.9030129

Author

Erofeeva, Victoria ; Granichin, Oleg ; Amelina, Natalia ; Ivanskiy, Yury ; Jiang, Yuming. / Distributed Tracking via Simultaneous Perturbation Stochastic Approximation-based Consensus Algorithm. 2019 IEEE 58th Conference on Decision and Control, CDC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. стр. 6050-6055 (IEEE Conference on Decision and Control).

BibTeX

@inproceedings{349b77642bb54b828af3af0308bf0055,
title = "Distributed Tracking via Simultaneous Perturbation Stochastic Approximation-based Consensus Algorithm",
abstract = "Networked systems comprised of multiple nodes with sensing, processing, and communication capabilities are able to provide more accurate estimates of some state of a dynamic process through communication between the network nodes. This paper considers the distributed estimation or tracking problem and focuses on a class of consensus normalized algorithms. A distributed algorithm consisting of two well-studied parts, namely, Simultaneous Perturbation Stochastic Approximation (SPSA) and the consensus approach is proposed for networked systems with uncertainties. Such combination allows us to relax the assumption regarding the strong convexity of the minimized mean-risk functional, which may not be fulfilled in the distributed optimization problems. For the proposed algorithm we get a mean squared upper bound of residual between estimates and unknown states. The theoretically established properties of proposed algorithm are illustrated by simulation results.",
keywords = "OPTIMIZATION, CONVEX",
author = "Victoria Erofeeva and Oleg Granichin and Natalia Amelina and Yury Ivanskiy and Yuming Jiang",
year = "2019",
month = dec,
doi = "10.1109/CDC40024.2019.9030129",
language = "English",
series = "IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6050--6055",
booktitle = "2019 IEEE 58th Conference on Decision and Control, CDC 2019",
address = "United States",
note = "58th IEEE Conference on Decision and Control, CDC 2019 ; Conference date: 11-12-2019 Through 13-12-2019",

}

RIS

TY - GEN

T1 - Distributed Tracking via Simultaneous Perturbation Stochastic Approximation-based Consensus Algorithm

AU - Erofeeva, Victoria

AU - Granichin, Oleg

AU - Amelina, Natalia

AU - Ivanskiy, Yury

AU - Jiang, Yuming

PY - 2019/12

Y1 - 2019/12

N2 - Networked systems comprised of multiple nodes with sensing, processing, and communication capabilities are able to provide more accurate estimates of some state of a dynamic process through communication between the network nodes. This paper considers the distributed estimation or tracking problem and focuses on a class of consensus normalized algorithms. A distributed algorithm consisting of two well-studied parts, namely, Simultaneous Perturbation Stochastic Approximation (SPSA) and the consensus approach is proposed for networked systems with uncertainties. Such combination allows us to relax the assumption regarding the strong convexity of the minimized mean-risk functional, which may not be fulfilled in the distributed optimization problems. For the proposed algorithm we get a mean squared upper bound of residual between estimates and unknown states. The theoretically established properties of proposed algorithm are illustrated by simulation results.

AB - Networked systems comprised of multiple nodes with sensing, processing, and communication capabilities are able to provide more accurate estimates of some state of a dynamic process through communication between the network nodes. This paper considers the distributed estimation or tracking problem and focuses on a class of consensus normalized algorithms. A distributed algorithm consisting of two well-studied parts, namely, Simultaneous Perturbation Stochastic Approximation (SPSA) and the consensus approach is proposed for networked systems with uncertainties. Such combination allows us to relax the assumption regarding the strong convexity of the minimized mean-risk functional, which may not be fulfilled in the distributed optimization problems. For the proposed algorithm we get a mean squared upper bound of residual between estimates and unknown states. The theoretically established properties of proposed algorithm are illustrated by simulation results.

KW - OPTIMIZATION

KW - CONVEX

UR - http://www.scopus.com/inward/record.url?scp=85082478251&partnerID=8YFLogxK

U2 - 10.1109/CDC40024.2019.9030129

DO - 10.1109/CDC40024.2019.9030129

M3 - Conference contribution

AN - SCOPUS:85082478251

T3 - IEEE Conference on Decision and Control

SP - 6050

EP - 6055

BT - 2019 IEEE 58th Conference on Decision and Control, CDC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 58th IEEE Conference on Decision and Control, CDC 2019

Y2 - 11 December 2019 through 13 December 2019

ER -

ID: 52781176