Abstract:
In today’s world, Specific emitter identification (SEI) has become crucial task in elec tronic warfare and signal intelligence that involves identifying a particular communication
device by examining the unique radio frequency (RF) signals it emits. The ability to dis criminate between different emitters is essential for ensuring the security and efficiency
of communication networks, spectrum management, and electronic warfare operations.
Existing emitter recognition methods often ignore the radio frequency (RF) fingerprint
details carried by the waveforms which are primarily susceptible to a specific application
scenario and radio environment and thus can be interfered with by unreliable RF features.
It has been found that deep learning methods have demonstrated effectiveness in this
task. This research introduces an innovative approach for Specific Emitter Identification
(SEI) utilizing a Savitzky-Golay filter for denoising and a Stacked Multivariate Convolu tional Neural Network (SMvCNN) architecture for classification. The inputs which are
fed to Stacked Multivariate Convolutional Neural Network (SMvCNN) are time domain,
frequency domain and phase of signals. By using this our proposed method surpasses
conventional machine learning classifiers, achieving an impressive classification accuracy
of 96% even under challenging conditions with a signal to noise ratio (SNR) of 5 dB.
The integration of the Savitzky-Golay filter for noise reduction and the SMvCNN model
demonstrates superior performance, underscoring its potential as a robust SEI technique
in real-world scenarios.
