Classifying aircraft based on sparse recovery and deep-learning

A hybrid CS-DL method for aircraft classification in complex electromagnetic environment is introduced. To classify aircraft from interfered radar echoes, the authors propose a novel classification method based on compressed sensing (CS) and deep-learning (DL). After recovering the spectrum polluted by jamming signals by using CS, they exploit sparse auto-encoder (SAE) to extract modulation features and then classify aircraft. The method is tested by 536 flights of three types of airplanes, and the results show that the correct classification rate reaches 75% even when 41% of the pulses are interfered.