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A look at Gaussian mixture reduction algorithms

David Crouse ; Peter Willett ; Krishna Pattipati ; Lennart Svensson (Institutionen för signaler och system, Signalbehandling)
14th International Conference on Information Fusion, Fusion 2011; Chicago, IL; 5 July 2011 through 8 July 2011 (2011)
[Konferensbidrag, refereegranskat]

We review the literature and look at two of the best algorithms for Gaussian mixture reduction, the GMRC (Gaussian Mixture Reduction via Clustering) and the COWA (Constraint Optimized Weight Adaptation) which has never been compared to the GMRC. We note situations that could yield invalid results (i.e., reduced mixtures having negative weight components) and offer corrections to this problem. We also generalize the GMRC to work with vector distributions. We then derive a brute-force approach to mixture reduction that can be used as a basis for comparison against other algorithms on small problems. The algorithms described in this paper can be used in a number of different domains. We compare the performance of the aforementioned algorithms along with a simpler algorithm by Runnalls’ for reducing random mixtures, as well as when used in a Gaussian mixture reduction-based tracking algorithm.

Nyckelord: Clustering, Gaussian mixture reduction, ISE, Nonlinear optimization, Tracking

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Denna post skapades 2011-10-06. Senast ändrad 2017-01-27.
CPL Pubid: 146883


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Institutionen för signaler och system, Signalbehandling (1900-2017)


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