In this paper, a new algorithm for distributed multi-target tracking in a sensor network is proposed. The main feature of that algorithm, combining the SPSA techniques and iterative averaging ("consensus algorithm"), is the ability to solve distributed optimization problems in presence of signals with fully uncertain distribution; the only assumption is the signal’s boundedness. As an example, we consider the multi-target tracking problem, in which the unknown signals include measurement errors and unpredictable target’s maneuvers; statistical properties of these signals are unknown. A special choice of weights in the algorithm enables its application to targets exhibiting different behaviors. An explicit estimate of the residual’s covariance matrix is obtained, which may be considered as a performance index of the algorithm. Theoretical results are illustrated by numerical simulations.

Weighted SPSA-based Consensus Algorithm for Distributed Cooperative Target Tracking / Erofeeva, Victoria; Granichin, Oleg; Granichina, Olga; Proskurnikov, Anton; Sergeenko, Anna. - ELETTRONICO. - (2021), pp. 1074-1079. ((Intervento presentato al convegno European Control Conference 2021 tenutosi a Delft, Netherlands nel 29 June-2 July 2021 [10.23919/ECC54610.2021.9655195].

Weighted SPSA-based Consensus Algorithm for Distributed Cooperative Target Tracking

Proskurnikov, Anton;
2021

Abstract

In this paper, a new algorithm for distributed multi-target tracking in a sensor network is proposed. The main feature of that algorithm, combining the SPSA techniques and iterative averaging ("consensus algorithm"), is the ability to solve distributed optimization problems in presence of signals with fully uncertain distribution; the only assumption is the signal’s boundedness. As an example, we consider the multi-target tracking problem, in which the unknown signals include measurement errors and unpredictable target’s maneuvers; statistical properties of these signals are unknown. A special choice of weights in the algorithm enables its application to targets exhibiting different behaviors. An explicit estimate of the residual’s covariance matrix is obtained, which may be considered as a performance index of the algorithm. Theoretical results are illustrated by numerical simulations.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2950733