Classifying aircraft based on sparse recovery and deep-learning

Abstract

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.

Keywords

• jamming
• signal classification
• learning (artificial intelligence)
• feature extraction
• neural nets
• radar cross-sections
• aerospace computing
• interference (signal)
• deep-learning
• sparse auto-encoder
• correct classification rate
• sparse recovery
• hybrid cs-dl
• aircraft classification
• complex electromagnetic environment
• interfered radar echoes
• novel classification method
• compressed sensing
• jamming signals
• modulation feature extraction