TY - GEN

T1 - Advanced SPSA-based Algorithm for Multi-Target Tracking in Distributed Sensor Networks

AU - Sergeenko, Anna

AU - Granichin, Oleg

AU - Proskurnikov, Anton V.

N1 - Funding Information: This work was supported SPbSU by the Russian Fund for Basic Research (project no. 20-01-00619). Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020/12/14

Y1 - 2020/12/14

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85099877501&partnerID=8YFLogxK

U2 - 10.1109/CDC42340.2020.9303942

DO - 10.1109/CDC42340.2020.9303942

M3 - Conference contribution

AN - SCOPUS:85099877501

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 2424

EP - 2429

BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 59th IEEE Conference on Decision and Control, CDC 2020

Y2 - 14 December 2020 through 18 December 2020

ER -