We consider a dynamic network of sensors that cooperate to estimate parameters of multiple targets. Each sensor can observe parameters of a few targets, reconstructing the trajectories of the remaining targets via interactions with “neighbouring” sensors. The multi-target tracking has to be provided in the face of uncertainties, which include unknown-but-bounded drift of parameters, noise in observations and distortions introduced by communication channels. To provide tracking in presence of these uncertainties, we employ a distributed algorithm, being an “offspring” of a consensus protocol and the stochastic gradient descent. The mathematical results on the algorithm’s convergence are illustrated by numerical simulations.

Consensus-based Distributed Algorithm for Multisensor-Multitarget Tracking under Unknown–but–Bounded Disturbances / Amelina, Natalia; Erofeeva, Victoria; Granichin, Oleg; Ivanskiy, Yury; Jiang, Yuming; Proskurnikov, Anton; Sergeenko, Anna. - ELETTRONICO. - 53:(2020), pp. 3589-3595. ((Intervento presentato al convegno 21th IFAC World Congress tenutosi a Virtuale (formalmente Berlin, Germania) [10.1016/j.ifacol.2020.12.1756].

Consensus-based Distributed Algorithm for Multisensor-Multitarget Tracking under Unknown–but–Bounded Disturbances

Proskurnikov, Anton;
2020

Abstract

We consider a dynamic network of sensors that cooperate to estimate parameters of multiple targets. Each sensor can observe parameters of a few targets, reconstructing the trajectories of the remaining targets via interactions with “neighbouring” sensors. The multi-target tracking has to be provided in the face of uncertainties, which include unknown-but-bounded drift of parameters, noise in observations and distortions introduced by communication channels. To provide tracking in presence of these uncertainties, we employ a distributed algorithm, being an “offspring” of a consensus protocol and the stochastic gradient descent. The mathematical results on the algorithm’s convergence 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/11583/2894612