Multi-target tracking with an adaptive $\delta-$GLMB filter

Abstract In multi-target tracking, targets can appear and disappear in the surveillance region, randomly varying the number of targets and their locations throughout 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 of measurement clutter (false alarms) produced by the sensor, and the probability of detection targets have to be taken into account to model the multi-target system as realistic as possible. In general, such information is not available. As a result, the tracking algorithms have to be supplied with intuitive guesses of these values, which usually results in inferior performances. Therefore, accurate inference of these parameters is paramount for achieving acceptable tracking performance in practice. In this paper, we propose a plug-and-play multi-target tracking algorithm based on the recent $\delta$-Generalized Labeled Multi-Bernoulli $\delta$-GLMB) filter which remove the guess work in determining the parameters of the target birth process, the detection probability, and clutter rate online. The simulation results of a tracking scenario with targets having linear and nonlinear motion models prove the efficacy of the proposed algorithm.