Consensus-based Distributed Algorithm for Multisensor-Multitarget Tracking under Unknown–but–Bounded Disturbances. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Amelina, Natalia; Erofeeva, Victoria; Granichin, Oleg; Ivanskiy, Yury; Jiang, Yuming; Proskurnikov, Anton; Sergeenko, Anna
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 3589
• Page End: 3595
• Publication Date: 2020
• Subjects: Sensor network -- randomized algorithms -- consensus -- multitarget tracking; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1756
• Languages: English
• ISSNs: 2405-8963

Abstract

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.