A Unified Anti-Jamming Design in Complex Environments Based on Cross-Modal Fusion and Intelligent Decision-Making

TL;DR

This paper introduces a unified framework combining interference recognition and intelligent anti-jamming strategy selection using cross-modal fusion and deep reinforcement learning, significantly improving accuracy and stability in complex electromagnetic environments.

Contribution

It presents a novel integrated approach that fuses time-frequency and time-domain features with attention mechanisms and employs a Deep Q-Network for adaptive anti-jamming strategy selection.

Findings

Achieves higher jamming recognition accuracy than SVM, VGG-16, and 2D-CNN.

Demonstrates faster reward convergence and more stable strategy generation than SARSA.

Improves anti-jamming effectiveness in complex electromagnetic environments.

Abstract

With the rapid development of radar jamming systems, especially digital radio frequency memory (DRFM), the electromagnetic environment has become increasingly complex. In recent years, most existing studies have focused solely on either jamming recognition or anti-jamming strategy design. In this paper, we propose a unified framework that integrates interference recognition with intelligent anti-jamming strategy selection. Specifically, time-frequency (TF) features of radar echoes are first extracted using both Short-Time Fourier Transform (STFT) and Smoothed Pseudo Wigner-Ville Distribution (SPWVD). A feature fusion method is then designed to effectively combine these two types of time-frequency representations. The fused TF features are further combined with time-domain features of the radar echoes through a cross-modal fusion module based on an attention mechanism. Finally, the recognition results, together with information obtained from the passive radar, are fed into a Deep Q-Network (DQN)-based intelligent anti-jamming strategy network to select jamming suppression waveforms. The key jamming parameters obtained by the passive radar provide essential information for intelligent decision-making, enabling the generation of more effective strategies tailored to specific jamming types. The proposed method demonstrates improvements in both jamming type recognition accuracy and the stability of anti-jamming strategy selection under complex environments. Experimental results show that our method achieves superior performance compared to Support Vector Machines (SVM), VGG-16, and 2D-CNN methods, with respective improvements of 1.41%, 2.5%, and 14.51% in overall accuracy. Moreover, in comparison with the SARSA algorithm, the designed algorithm achieves faster reward convergence and more stable strategy generation.