Title :
The Multiple Model CPHD Tracker
Author :
Georgescu, Ramona ; Willett, Peter
Author_Institution :
Electr. & Comput. Eng. Dept., Univ. of Connecticut, Storrs, CT, USA
fDate :
4/1/2012 12:00:00 AM
Abstract :
The probability hypothesis density (PHD) is a practical approximation to the full Bayesian multi-target filter. The cardinalized PHD (CPHD) filter was proposed to deal with the “target death” problem of the PHD filter. A multiple-model PHD exists; in this work, a multiple model version of the considerably more complex CPHD filter is derived. It is implemented using Gaussian mixtures, and a track management (for display and scoring) strategy is developed.
Keywords :
Bayes methods; Gaussian processes; target tracking; Bayesian multitarget filter; Gaussian mixtures; multiple model CPHD tracker; probability hypothesis density; target death problem; track management; Bayesian methods; Equations; Hidden Markov models; Joints; Mathematical model; Target tracking; CPHD; Cardinalized probability hypothesis density filter; MMCPHD; MMPHD; PHD; multiple model;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2012.2183128
