Greedy Reduction Algorithms for Mixtures of Exponential Family
Tohid Ardeshiri, Karl Granström, Emre Özkan, Umut Orguner · IEEE Signal Processing Letters · 2014
In this letter, we propose a general framework for greedy reduction of mixture densities of exponential family. The performances of the generalized algorithms are illustrated both on an artificial example where randomly generated mixture densities are reduced and on a target tracking scenario where the reduction is carried out in the recursion of a Gaussian inverse Wishart probability hypothesis density (PHD) filter.