Multi-target PHD filtering: proposition of extensions to the multi-sensor case

Common difficulties in multi-target tracking arise from the fact that the system state and the collection of measures are unordered and their size evolve randomly through time. The random finite set theory provides a powerful framework to cope with these issues. This document focuses more particularly on the PHD (Probability Hypothesis Density) filter proposed by Mahler. The first part of this report is a synthesis of Mahler's work and aims at providing a thorough description of the construction of the single-sensor PHD filter. Then, based on a few leads provided by Mahler, the second part proposes several extensions of this filter to the multi-sensor case.