Adaptive signal processing of on-orbit radio frequency lightning recordings using overcomplete chirplet dictionaries

Ongoing research at Los Alamos National Laboratory studies the Earth's radio frequency (RF) transient background utilizing satellite-based RF observations of terrestrial lightning. Such impulsive signals are dispersed as they travel through the ionosphere and appear as nonlinear chirps at a receiver on-orbit. Signals of interest are typically observed in the presence of additive noise and structured clutter, including gated and continuous-wave (CW) sources. Detection and classification of such non-stationary signals against a complex, non-stationary background can present challenges for standard physics-based approaches. The 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 representations in overcomplete analytical dictionaries. We choose a dictionary based on Gabor chirplets, which is designed to represent both pulses (chirping or non-chirping) and CW signals in very few representative elements from the dictionary. One feature extraction approach is based on obtaining sparse representations of our data using a matching pursuit search of the dictionary. A second approach is based on using a frame operator on the dictionary to obtain a dense representation of our data. We explore robustness of extracted features to changes in background clutter and noise levels. Both feature extraction algorithms will be used in conjunction with statistical classifiers to explore classification performance of major lightning types. Performance will be evaluated both qualitatively, as well as quantitatively using a small validated test set. We present preliminary results of our work and discuss future areas of development.