Classification of transient signals using sparse representations over adaptive dictionaries

Daniela I. Moody; Steven P. Brumby; Kary L. Myers; Norma H. Pawley

doi:10.1117/12.883341

3 June 2011 Classification of transient signals using sparse representations over adaptive dictionaries

Daniela I. Moody, Steven P. Brumby, Kary L. Myers, Norma H. Pawley

Proceedings Volume 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX; 805804 (2011) https://doi.org/10.1117/12.883341
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Automatic classification of broadband transient radio frequency (RF) signals is of particular interest in persistent surveillance applications. Because such transients are often acquired in noisy, cluttered environments, and are characterized by complex or unknown analytical models, feature extraction and classification can be difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. Conventional representations using fixed (or analytical) orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of transients, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They do not usually lead to sparse decompositions, and require separate feature selection algorithms, creating additional computational overhead. Pursuit-type decompositions over analytical, redundant dictionaries yield sparse representations by design, and work well for target signals in the same function class as the dictionary atoms. The pursuit search however has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. Our approach builds on the image analysis work of Mairal et al. (2008) to learn a discriminative dictionary for RF transients directly from data without relying on analytical constraints or additional knowledge about the signal characteristics. We then use a pursuit search over this dictionary to generate sparse classification features. We demonstrate that our learned dictionary is robust to unexpected changes in background content and noise levels. The target classification decision is obtained in almost real-time via a parallel, vectorized implementation.

Citation Download Citation

Daniela I. Moody, Steven P. Brumby, Kary L. Myers, and Norma H. Pawley "Classification of transient signals using sparse representations over adaptive dictionaries", Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 805804 (3 June 2011); https://doi.org/10.1117/12.883341

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