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

• 成果类型: Article
• 署名作者: Granichin, Oleg; Erofeeva, Victoria; Ivanskiy, Yury; Jiang, Yuming
• 署名单位: Saint Petersburg State University; Norwegian University of Science & Technology (NTNU)
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3024169
• 发表日期: 2021
• 页码: 3710-3717
• 关键词: sensors; Approximation algorithms; optimization; Noise measurement; Perturbation methods; Network topology; Upper bound; Arbitrary noise; Consensus algorithm; distributed tracking; multiagent networks; Randomized algorithm; simultaneous perturbation stochastic approximation (SPSA); stochastic stability; tracking performance; unknown-but-bounded disturbances

摘要

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.