Signal classification of satellite-based recordings of radiofrequency (RF) transients using data-adaptive dictionaries
Daniela I. Moody, David Aitchison Smith, Tess E. Light, Matthew J. Heavner, Timothy D. Hamlin, David M. Suszcynsky · 2013
Ongoing research at Los Alamos National Laboratory (LANL) studies the Earth's radiofrequency (RF) background utilizing satellite-based RF observations of terrestrial lightning. The Fast On-orbit Recording of Transient Events (FORTE) satellite provided a rich satellite lightning database, that has been previously used for some event classification. We now develop and implement new event classification capability on the FORTE database using state-of-the-art adaptive signal processing combined with compressive sensing and machine learning techniques. The focus of our work is improved feature extraction using sparse representations in data-adaptive dictionaries. We explore two dictionary approaches: dictionaries learned directly from data, and analytical, over-complete dictionaries. Discriminative dictionaries learned directly from data do not rely on analytical constraints or knowledge about the signal characteristics, and provide sparse representations that can perform well when used with a statistical classifier. Pursuit-type decompositions over analytical, over-complete dictionaries yield sparse representations by design and can work well for signals in the same function class as the dictionary atoms. We present preliminary results of our work and discuss performance and future development.