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 the general lack of knowledge that target-tracking practitioner must deal with when working real data. also introduce study variants notions point process intensity function, which lead derivation an analogue probability hypothesis density (PHD) filter. The gains provided by considered in terms flexibility loss some abilities PHD filter possesses; particular estimation number targets integration function. Yet, proposed recursion displays advantages such as facilitating introduction observation-driven birth schemes absence information initial scene. performance approach is demonstrated simulated