Collaborative and distributive intelligence in radar systems: Enhancing electronic jamming discrimination

Abstract

The precise discrimination of radar jamming signals is decisive in executing effective electronic counter-countermeasures (ECCM). Data-driven deep learning (DL) models have proven effective for this task, but challenges persist in addressing critical real-world issues such as limited data sharing, time- and location-dependent variations in hostile interferences, real-time adaptability to evolving jamming tactics, and imbalanced data distribution. In this paper, a novel approach is proposed that employs federated learning (FL) for training radar signal jamming classifiers individually on a set of devices, aggregating, sharing, and continuously updating the knowledge. This approach enables privacy-preserving training, eliminating access to client-local data or centralized data storage, ensuring knowledge sharing, and resilient response to jamming. This work delineates a collaborative and distributive learning framework for radar jamming signals, employing two hybrid models within an FL platform. A deep spectra spatio-temporal discriminator (DSSTD) and a shallow spectra-temporal feed-forward self-attention-driven discriminator (SSTFSAD) network have been implemented as classifiers on a distributed arrangement. The time–frequency attributes of radar jamming signals as 2D-scalograms individually train these models across remote FL nodes. Extensive evaluations are conducted in FL environments under independently and identically distributed (IID) and non-IID data configurations, simulating real-world settings with diverse data distributions. Experimental results demonstrate that both proposed approaches are effective, with FL-driven DSSTD outperforming FL-driven SSTFSAD by 7.7% in IID and 6.8% in non-IID setups, even at -5 dB JNR. These results highlight the robustness and adaptability of the FL-driven DSSTD model, offering a significant advancement in radar jamming signal discrimination for electronic warfare applications.