Sparse classification of rf transients using chirplets and learned dictionaries

We assess the performance of a sparse classification approach for radiofrequency (RF) transient signals using dictionaries adapted to the data. We explore two approaches: pursuit-type decompositions over analytical, over-complete dictionaries, and dictionaries learned directly from data. Pursuit-type decompositions over analytical, over-complete dictionaries yield sparse representations by design and can work well for target signals in the same function class as the dictionary atoms. Discriminative dictionaries learned directly from data do not rely on analytical constraints or additional knowledge about the signal characteristics, and provide sparse representations that can perform well when used with a statistical classifier. We present classification results for learned dictionaries on simulated test data, and discuss robustness compared to conventional Fourier methods. We draw from techniques of adaptive feature extraction, statistical machine learning, and image processing.