Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks

TL;DR

This paper introduces a novel deep learning approach using feature fusion convolutional neural networks to automatically classify intra-pulse radar signal modulations, outperforming existing methods.

Contribution

The work presents a new FF-CNN technique that combines features from spectrograms and phase outliers for improved radar signal modulation classification.

Findings

FF-CNN outperforms current state-of-the-art methods

The approach is scalable across various modulation types

Simulation results validate the effectiveness of the proposed method

Abstract

Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. Re-assigned spectrogram of measured radar signal and detected outliers of its instantaneous phases filtered by a special function are used for training multiple convolutional neural networks. Automatically extracted features from the networks are fused to distinguish frequency and phase modulated signals. Simulation results show that the proposed FF-CNN (Feature Fusion based Convolutional Neural Network) technique outperforms the current state-of-the-art alternatives and is easily scalable among broad range of modulation types.