{'fraction_non_alphanumeric': 0.014660493827160493, 'fraction_numerical': 0.0, 'mean_word_length': 6.010810810810811, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Adaptive sparse signal processing for discrimination of satellite-based radiofrequency (RF) recordings of lightning events

For over two decades, Los Alamos National Laboratory programs have included an active research effort utilizing satellite observations of terrestrial lightning to learn more about the Earth’s RF background. The FORTE satellite provided a rich satellite lightning database, which has been previously used for some event classification, and remains relevant for advancing lightning research. Lightning impulses are dispersed as they travel through the ionosphere, appearing as nonlinear chirps at the receiver on orbit. The data processing challenge arises from the combined complexity of the lightning source model, the propagation medium nonlinearities, and the sensor artifacts. We continue to develop modern event classification capability on the FORTE database using adaptive signal processing combined with compressive sensing techniques. The focus of our work is improved feature extraction using sparse representations in overcomplete analytical dictionaries. We explore two possible techniques for detecting lightning events, and showcase the algorithms on few representative data examples. We present preliminary results of our work and discuss future development.