Consensus-based Distributed Algorithm for Multisensor-Multitarget Tracking under Unknown-but-Bounded Disturbances
Erofeeva, Victoria; Granichin, Oleg; Ivanskiy, Yury; Jiang, Yuming; Proskurnikov, Anton; Sergeenko, Anna
Peer reviewed, Journal article
Published version
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https://hdl.handle.net/11250/2775038Utgivelsesdato
2020Metadata
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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.