A MIXED GM/SMC IMPLEMENTATION OF THE PROBABILITY HYPOTHESIS DENSITY FILTER

The Probability Hypothesis Density (PHD) filter is a re-cent solution for tracking an unknown number of targets in a multi-object environment. The PHD filter cannot be computed exactly, but popular implementations include Gaussian Mixture (GM) and Sequential Monte Carlo (SMC) based algorithms. GM implementations suffer from prun-ing and merging approximations, but enable to extract the states easily; on the other hand, SMC implementations are of interest if the discrete approximation is relevant, but are penalized by the difficulty to guide particles to-wards promising regions and to extract the states. In this paper, we propose a mixed GM/SMC implementation of the PHD filter which does not suffer from the above men-tioned drawbacks. Due to the SMC part, our algorithm can be used in models where the GM implementation is unavailable; but it also benefits from the easy state ex-traction of GM techniques, without requiring pruning or merging approximations. Our algorithm is validated on simulations. 1