For the past two decades, there has been an ongoing research effort at Los Alamos National Laboratory to learn more
about 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 RF lighting database, comprising of five
years of data recorded from its two RF payloads. While some classification work has been done previously on the
FORTE RF database, application of modern pattern recognition techniques may advance lightning research in the
scientific community and potentially improve on-orbit processing and event discrimination capabilities for future
satellite payloads. 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 learned dictionaries. Conventional
localized data representations for RF transients using analytical dictionaries, such as a short-time Fourier basis or
wavelets, can be suitable for analyzing some types of signals, but not others. Instead, we learn RF dictionaries directly
from data, without relying on analytical constraints or additional knowledge about the signal characteristics, using
several established machine learning algorithms. Sparse classification features are extracted via matching pursuit search
over the learned dictionaries, and used in conjunction with a statistical classifier to distinguish between lightning types.
We present preliminary results of our work and discuss classification scenarios and future development.