2.5 Random Finite Set Methods

The Probability Hypothesis Density (PHD) filter [10][11][89] is a Bayesian multitarget tracking estimator initially proposed in [56].

The PHD filter is developed based on the

Random Finite Set (RFS) theory,
point processes, and
Finite Set Statistics (FISST).

특징 : It estimates all the targets states at once, as a multitarget state, projected on the single-target space.

The PHD filter has been shown an effective way of tracking a time-varying multiple number of targets that avoids model-data association problems [56].

변형/개선

A Gaussian mixture implementation of PHD filter (GM-PHD) is presented in [89].
For nonlinear measurements, the Sequential Monte Carlo (SMC) implementation of the PHD filter is presented in [10].

Tracking