Multi-Target PHD Tracking and Classification Using Imprecise Likelihoods

This article proposes a method to track and classify multiple target based on kinematics data. On one hand, tracking is performed using a Probability Hypothesis Density (PHD) filter avoiding the association stage, necessary for many tracking algorithms. On the other hand, Belief Functions and imprecise probabilities are used for the classification task, reducing errors from standard Bayesian classifiers when data are ambiguous. The proposed method is evaluated on several scenarios of multiple aircraft tracking. It is shown in particular that when the number of targets is varying, the proposed approach leads to a reduced number of false created target and improves the classification task over a standard Bayesian classifier where both belief function based classifier and imprecise probabilities classifier give the same result.