A linear algorithm for multi-target tracking in the context of possibility theory

We present a modelling framework for multi-target tracking based on possibility theory and illustrate its ability to account for the general lack of knowledge that the target-tracking practitioner must deal with when working with real data. We argue that the flexibility of this approach decreases the risks of misspecification and facilitates the modelling of complex phenomena. We also introduce and study variants of the notions of point process and intensity function, which lead to the derivation of an analogue of the probability hypothesis density (PHD) filter. The gains provided by the considered modelling framework in terms of flexibility lead to the loss of some of the abilities that the PHD filter possesses; in particular the estimation of the number of targets by integration of the intensity function. Yet, the proposed recursion displays a number of advantages such the availability of proper observation-driven birth schemes as well as the ability to perform multi-sensor fusion in a natural way