Radar Waveform Recognition Using Fourier-Based Synchrosqueezing Transform and CNN
- Resource Type
- Conference
- Authors
- Kong, Gyuyeol; Koivunen, Visa
- Source
- 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019 IEEE 8th International Workshop on. :664-668 Dec, 2019
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Time-frequency analysis
Radar imaging
Convolution
Signal to noise ratio
Frequency modulation
Transforms
Choi-Williams distribution
convolutional neural network
Fourier-based synchrosqueezing transform
radar waveform recognition
- Language
In this paper the problem of recognizing radar waveforms is addressed. Waveform classification is needed in spectrum sharing and radar-communications coexistence, cognitive radars and signal intelligence. Different radar waveforms exhibit different properties in time-frequency domain. We propose a deep learning method for waveform classification. The received signal is processed with Fourier synchrosqueezing transform that has excellent properties in revealing time-varying behavior, rate of, strength and number of oscillatory components in received signals. The resulting time-frequency description is represented as a bivariate image that is fed into a convolutional neural network. The proposed method has superior performance over the widely used the Choi-Williams distribution (CWD) method in distinguishing the polyphase waveforms even at low signal-to-noise ratio regime.