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Multi-target tracking with an adaptive $δ-$GLMB filter

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Document pages: 14 pages

Abstract: Abstract In multi-target tracking, targets can appear and disappear in thesurveillance region, randomly varying the number of targets and their locationsthroughout the tracking process. Moreover, apart from measurement noise,observations of the targets are corrupted by misdetections, and false alarms.Therefore, prior information such as the target birth locations, amount ofmeasurement clutter (false alarms) produced by the sensor, and the probabilityof detection targets have to be taken into account to model the multi-targetsystem as realistic as possible. In general, such information is not available.As a result, the tracking algorithms have to be supplied with intuitive guessesof these values, which usually results in inferior performances. Therefore,accurate inference of these parameters is paramount for achieving acceptabletracking performance in practice. In this paper, we propose a plug-and-playmulti-target tracking algorithm based on the recent $ delta$-GeneralizedLabeled Multi-Bernoulli $ delta$-GLMB) filter which remove the guess work indetermining the parameters of the target birth process, the detectionprobability, and clutter rate online. The simulation results of a trackingscenario with targets having linear and nonlinear motion models prove theefficacy of the proposed algorithm.

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