DocumentCode :
2803836
Title :
An efficient particle filtering technique on the Grassmann manifold
Author :
Rentmeesters, Quentin ; Absil, P.-A. ; Van Dooren, Paul ; Gallivan, Kyle ; Srivastava, Anuj
Author_Institution :
Univ. Catholique de Louvain, Louvain-la-Neuve, Belgium
fYear :
2010
fDate :
14-19 March 2010
Firstpage :
3838
Lastpage :
3841
Abstract :
Subspace tracking methods are widespread in signal and image processing. To reduce the influence of perturbations or outliers on the measurements, some authors have used a stochastic piecewise constant velocity model on the Grassmann manifold. This paper presents an efficient way to simulate such a model using a particular representation of the Grassmann manifold. By doing so, we can reduce the spatial and time complexity of filtering techniques based on this model. We also propose an approximation of this system which can be computed in a finite number of operations and show similar results if the subspace variation is slow.
Keywords :
particle filtering (numerical methods); stochastic processes; target tracking; Grassmann manifold; image processing; particle filtering; stochastic piecewise constant velocity model; subspace tracking; subspace variation; time-varying subspace learning; Computational efficiency; Computational modeling; Direction of arrival estimation; Filtering; Filters; Image processing; Signal processing; Stochastic processes; Velocity measurement; Video signal processing; Grassmann manifold; particle filtering; time-varying subspace learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
ISSN :
1520-6149
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2010.5495828
Filename :
5495828
Link To Document :
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