Abstract
Radar is widely used in aviation, meteorology and military fields, and
radar pulse signal detection has become an indispensable and essential
function of cognitive radio systems as well as electronic warfare
systems. In this paper, we propose a deep learning based radar signal
detection method. Firstly, we propose a detection method based on raw
in-phase and quadrature (IQ) input, which utilizes a convolutional
neural network (CNN) to automatically learn the features of radar pulse
signals and noises, so as to accomplish the detection task. To further
reduce the computational complexity, we also propose a hybrid detection
method that combines compressed sensing (CS) and deep learning, which
reduces the length of signal by compressed downsampling, and then feeds
the compressed signal to the CNN for detection. Extensive simulation
results show that our proposed IQ-based method outperforms the
traditional short-time Fourier transform method as well as three
existing deep learning-based detection methods in terms of probability
of detection. Furthermore, our proposed IQ-CS-based method is able to
achieve satisfactory detection performance with significantly reduced
computational complexity.