TY - JOUR

T1 - Simultaneous Perturbation Stochastic Approximation-Based Consensus for Tracking Under Unknown-But-Bounded Disturbances

AU - Granichin, Oleg

AU - Erofeeva, Victoria

AU - Ivanskiy, Yury

AU - Jiang, Yuming

N1 - Funding Information: Manuscript received June 21, 2020; accepted September 4, 2020. Date of publication September 15, 2020; date of current version July 28, 2021. This work was supported in part by the Russian Fund for Basic Research under Project 20-01-00619 and in part of experimental results in Section V by the Russian Science Foundation under Project 19-71-10012. Recommended by Associate Editor Z. Gao. (Corresponding author: Oleg Granichin.) Oleg Granichin, Victoria Erofeeva, and Yury Ivanskiy are with Saint Petersburg State University (Science and Educational Center of Mathematical Robotics and Artificial Intelligence), 198504 St. Petersburg, Russia (e-mail: o.granichin@spbu.ru; victoria@grenka.net; ivanskiy.yuriy@gmail.com). Publisher Copyright: © 1963-2012 IEEE.

PY - 2021/8

Y1 - 2021/8

N2 - We consider a setup where a distributed set of sensors working cooperatively can estimate an unknown signal of interest, whereas any individual sensor cannot fulfill the task due to lack of necessary information diversity. This article deals with these kinds of estimation and tracking problems and focuses on a class of simultaneous perturbation stochastic approximation (SPSA)-based consensus algorithms for the cases when the corrupted observations of sensors are transmitted between sensors with communication noise and the communication protocol has to satisfy a prespecified cost constraints on the network topology. Sufficient conditions are introduced to guarantee the stability of estimates obtained in this way, without resorting to commonly used but stringent conventional statistical assumptions about the observation noise, such as randomness, independence, and zero mean. We derive an upper bound of the mean square error of the estimates in the problem of unknown time-varying parameters tracking under unknown-but-bounded observation errors and noisy communication channels. The result is illustrated by a practical application to the multisensor multitarget tracking problem.

AB - We consider a setup where a distributed set of sensors working cooperatively can estimate an unknown signal of interest, whereas any individual sensor cannot fulfill the task due to lack of necessary information diversity. This article deals with these kinds of estimation and tracking problems and focuses on a class of simultaneous perturbation stochastic approximation (SPSA)-based consensus algorithms for the cases when the corrupted observations of sensors are transmitted between sensors with communication noise and the communication protocol has to satisfy a prespecified cost constraints on the network topology. Sufficient conditions are introduced to guarantee the stability of estimates obtained in this way, without resorting to commonly used but stringent conventional statistical assumptions about the observation noise, such as randomness, independence, and zero mean. We derive an upper bound of the mean square error of the estimates in the problem of unknown time-varying parameters tracking under unknown-but-bounded observation errors and noisy communication channels. The result is illustrated by a practical application to the multisensor multitarget tracking problem.

KW - Sensors

KW - Approximation algorithms

KW - Optimization

KW - Noise measurement

KW - Perturbation methods

KW - Network topology

KW - Upper bound

KW - Arbitrary noise

KW - consensus algorithm

KW - distributed tracking

KW - multiagent networks

KW - randomized algorithm

KW - simultaneous perturbation stochastic approximation (SPSA)

KW - stochastic stability

KW - tracking performance

KW - unknown-but-bounded disturbances

KW - arbitrary noise

KW - SPSA

KW - unknown- but-bounded disturbances

KW - Distributed tracking

KW - simultaneous perturbation stochastic approximation

KW - multi-agent networks

UR - http://www.scopus.com/inward/record.url?scp=85091289375&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/fdc6d931-0f03-339c-8754-885d9e29d46a/

U2 - 10.1109/TAC.2020.3024169

DO - 10.1109/TAC.2020.3024169

M3 - статья

AN - SCOPUS:85091289375

VL - 66

SP - 3710

EP - 3717

JO - IEEE Transactions on Automatic Control

JF - IEEE Transactions on Automatic Control

SN - 0018-9286

IS - 8

M1 - 9198090

ER -