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Merging-based forward-backward smoothing on Gaussian mixtures

Abu Sajana Rahmathullah (Institutionen för signaler och system, Signalbehandling) ; Lennart Svensson (Institutionen för signaler och system, Signalbehandling) ; Daniel Svensson (Institutionen för signaler och system, Signalbehandling)
17th International Conference on Information Fusion, FUSION 2014; Salamanca; Spain; 7 July 2014 through 10 July 2014 p. Art. no. 6916248. (2014)
[Konferensbidrag, refereegranskat]

Conventional forward-backward smoothing (FBS) for Gaussian mixture (GM) problems are based on pruning methods which yield a degenerate hypothesis tree and often lead to underestimated uncertainties. To overcome these shortcomings, we propose an algorithm that is based on merging components in the GM during filtering and smoothing. Compared to FBS based on the N-scan pruning, the proposed algorithm offers better performance in terms of track loss, root mean squared error (RMSE) and normalized estimation error squared (NEES) without increasing the computational complexity.

Nyckelord: data association; filtering; forward-backward smoothing; Gaussian mixtures; smoothing



Denna post skapades 2014-05-27. Senast ändrad 2017-01-27.
CPL Pubid: 198613

 

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