
Proceedings Paper
Radio frequency (RF) transient classification using sparse representations over learned dictionariesFormat | Member Price | Non-Member Price |
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Paper Abstract
Automatic classification of transitory or pulsed radio frequency (RF) signals is of particular interest in persistent
surveillance and remote sensing applications. Such transients are often acquired in noisy, cluttered environments, and
may be characterized by complex or unknown analytical models, making feature extraction and classification difficult.
We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. We compare
two dictionary learning methods from the image analysis literature, the K-SVD algorithm and Hebbian learning, and
extend them for use with RF data. Both methods allow us to learn discriminative RF dictionaries directly from data
without relying on analytical constraints or additional knowledge about the expected signal characteristics. We then use
a pursuit search over the learned dictionaries to generate sparse classification features in order to identify time windows
that contain a target pulse. In this paper we compare the two dictionary learning methods and discuss how their
performance changes as a function of dictionary training parameters. We demonstrate that learned dictionary techniques
are suitable for pulsed RF analysis and present results with varying background clutter and noise levels.
Paper Details
Date Published: 27 September 2011
PDF: 8 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381S (27 September 2011); doi: 10.1117/12.898894
Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)
PDF: 8 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381S (27 September 2011); doi: 10.1117/12.898894
Show Author Affiliations
Daniela I. Moody, Los Alamos National Lab. (United States)
Univ. of Maryland (United States)
Steven P. Brumby, Los Alamos National Lab. (United States)
Univ. of Maryland (United States)
Steven P. Brumby, Los Alamos National Lab. (United States)
Kary L. Myers, Los Alamos National Lab. (United States)
Norma H. Pawley, Los Alamos National Lab. (United States)
Norma H. Pawley, Los Alamos National Lab. (United States)
Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)
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