Open AccessClassifying aircraft based on sparse recovery and deep‐learning
Author(s) -
Wenying Wang,
Yao Wei,
Xuanxuan Zhen,
Hui Yu,
Ruqi Wang
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0633
Subject(s) - computer science , radar , jamming , artificial intelligence , autoencoder , pattern recognition (psychology) , deep learning , compressed sensing , encoder , exploit , remote sensing , geology , telecommunications , physics , computer security , thermodynamics , operating system
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.