Possibility Generalized Labeled Multi-Bernoulli Filter for Multi-Target Tracking Under Epistemic Uncertainty

This paper presents a flexible modeling framework for multi-target tracking based on the theory of Outer Probability Measures (OPMs). The notion labeled uncertain finite set is introduced and utilized as basis to derive possibilistic analog $\delta$-Generalized Labeled Multi-Bernoulli ($\delta$-GLMB) filter, in which uncertainty system represented by possibility functions instead probability distributions. proposed method inherits capability standard probabilistic notation="LaTeX">$\delta$-GLMB filter yield joint state, number, trajectory estimates multiple appearing disappearing targets. Beyond that, it capable account epistemic due ignorance or partial knowledge regarding system, e.g., absence complete information dynamical model parameters (e.g., detection, birth) initial number state newborn features developed are demonstrated using two simulated scenarios.