Research on Multi-Feature Fusion and Lightweight Recognition for Radar Compound Jamming

TL;DR

This paper introduces a lightweight network for identifying complex radar jamming signals in challenging environments using multi-feature fusion and attention mechanisms.

Contribution

The novel contribution is a lightweight, high-accuracy network for radar compound jamming recognition using multi-branch feature fusion and an improved GSENet module.

Findings

The proposed network achieves over 87% recognition accuracy for seven jamming types at low JNR conditions.

The model maintains a parameter count below 0.14 M, ensuring low complexity.

The network effectively balances performance and model efficiency for ECCM applications.

Abstract

To recognize radar compound jamming under complex electromagnetic environments, this paper proposes a lightweight multi-feature fusion network for compound jamming recognition. Three complementary time–frequency representations are employed to extract various features of compound jamming, which are processed by a multi-branch architecture for parallel, multi-scale feature learning. Attention mechanisms are incorporated to enhance the discriminative characteristics of jamming, and a weighted fusion strategy is adopted to integrate multi-channel features effectively. Furthermore, an improved lightweight module, GSENet, is introduced to construct the recognition network with low complexity. Experiments on simulated radar jamming datasets demonstrate that the proposed network achieves over 87% recognition accuracy for seven compound jamming types under low jamming-to-noise ratio (JNR) conditions while maintaining a parameter count below 0.14 M. These results indicate that the proposed network provides an effective trade-off between recognition performance and model complexity, making it suitable for electronic counter-countermeasure (ECCM) applications.

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