Robust Radar HRRP Recognition under Non-uniform Jamming Based on Complex-valued Frequency Attention Network

TL;DR

This paper introduces a complex-valued frequency attention network that enhances radar HRRP recognition robustness against non-uniform jamming by adaptively filtering spectral distortions, significantly improving accuracy in challenging electromagnetic environments.

Contribution

The paper presents a novel complex-valued frequency attention network with an adaptive spectral filtering module for robust HRRP recognition under non-uniform jamming conditions.

Findings

Achieves nearly 9% higher recognition accuracy under severe jamming.

Effectively suppresses jamming interference while preserving target information.

Maintains low computational overhead compared to traditional methods.

Abstract

Complex electromagnetic environments, often containing multiple jammers with different jamming patterns, produce non-uniform jamming power across the frequency spectrum. This spectral non-uniformity directly induces severe distortion in the target's HRRP, consequently compromising the performance and reliability of conventional HRRP-based target recognition methods. This paper proposes a novel, end-to-end trained network for robust radar target recognition. The core of our model is a CFA module that operates directly on the complex spectrum of the received echo. The CFA module learns to generate an adaptive frequency-domain filter, assigning lower weights to bands corrupted by strong jamming while preserving critical target information in cleaner bands. The filtered spectrum is then fed into a classifier backbone for recognition. Experimental results on simulated HRRP data with various jamming combinations demonstrate our method's superiority. Notably, under severe jamming conditions, our model achieves a recognition accuracy nearly 9% higher than traditional model-based approaches, all while introducing negligible computational overhead. This highlights its exceptional performance and robustness in challenging jamming environments.