Advanced SPSA-based Algorithm for Multi-Target Tracking in Distributed Sensor Networks

Anna Sergeenko, Oleg Granichin, Anton V. Proskurnikov

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Tracking of multiple targets is a classical problem in signal processing that arises in many applications, e.g. air, maritime and road traffic control. Networks of autonomous sensors serve as desirable platforms for multi-target tracking in view of their redundancy and reconfigurability. The networked implementation, however, makes it impossible to use classical centralized approaches to filtering, since each sensor has limited computational capabilities and restricted access to the measurements of other sensors. Besides topological constraints (each sensor can interact only to a few adjacent nodes of a network), communication between sensors can be restricted, due to e.g. limited capacity of communication channels, delays and data distortions.In this paper, we propose a new algorithm for distributed multi-target tracking in a sensor network. The algorithm is based on the seminal idea of simultaneous perturbation stochastic approximation (SPSA), being a special case of stochastic gradient descent algorithm. The important feature of the SPSA method is the ability to solve optimization (in particular, optimal tracking) problems in the presence of arbitrary unknown (but bounded) disturbances and time-varying parameters of the system. These uncertainties need not be random, and even if they are random, one need not know their statistical characteristics. We provide the mathematical results on stabilization of the mean-square estimation error and analyze its dependence on the choice of step-size parameters. Theoretical results are illustrated by numerical simulations.

Original languageEnglish
Title of host publication2020 59th IEEE Conference on Decision and Control, CDC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2424-2429
Number of pages6
ISBN (Electronic)9781728174471
DOIs
StatePublished - 14 Dec 2020
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of
Duration: 14 Dec 202018 Dec 2020

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2020-December
ISSN (Print)0743-1546

Conference

Conference59th IEEE Conference on Decision and Control, CDC 2020
CountryKorea, Republic of
CityVirtual, Jeju Island
Period14/12/2018/12/20

Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

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