JointHRRP-Net: A Statistically Constrained Decoupling Network for Joint Target and Jamming Recognition in Composite Jamming

2026-05-19

AI Summary

This work addresses the significant performance degradation in high-resolution range profile (HRRP)-based radar target recognition under composite interference, where interference and target echoes are highly coupled in the HRRP domain, diminishing the discriminability of scattering features. To tackle this challenge, the authors propose JointHRRP-Net, a unified framework that incorporates a correlation-guided statistical constraint module to disentangle target-dominant and interference-dominant latent representations from mixed HRRPs. By integrating multi-scale temporal encoding with a dual-expert decision mechanism, the framework achieves, for the first time, joint single-label target classification and multi-label interference-type identification. Experimental results demonstrate that the proposed method consistently outperforms state-of-the-art baselines across varying signal-to-jamming ratios (SJR) and signal-to-noise ratios (SNR), while open-set evaluations confirm its strong discriminative capability and robustness toward unseen targets.

Abstract

High-resolution range profile (HRRP)-based radar automatic target recognition suffers from severe performance degradation in composite jamming environments. Active jamming introduces suppression- and deception-related components into the received range profile. After pulse compression, these components are coupled with target echoes in the HRRP domain, making target-related scattering peaks difficult to distinguish and weakening feature separability. To address this problem, this paper proposes JointHRRP-Net, a unified framework for joint target-jamming recognition. A statistically constrained decoupling module is first developed to generate target-dominant and jamming-dominant latent branches from the mixed HRRP representation. Correlation-guided statistical constraints are imposed to suppress redundant cross-branch information and alleviate target-jamming feature entanglement. A multi-scale temporal encoding module is then designed to model local scattering structures and long-range range-cell dependencies, followed by a dual-expert decision module for single-label target classification and multi-label jamming classification. Experiments under diverse signal-to-jamming ratio (SJR) and signal-to-noise ratio (SNR) levels demonstrate that JointHRRP-Net outperforms representative baseline methods in both target recognition and composite jamming recognition. Open-set evaluation further shows that the learned target representation remains discriminative for unknown-target rejection. These results demonstrate the effectiveness and robustness of JointHRRP-Net in composite jamming scenarios.

Problem

• composite jamming
• HRRP
• target recognition
• feature entanglement
• radar automatic target recognition

Innovation

• statistically constrained decoupling
• composite jamming recognition
• multi-scale temporal encoding
• dual-expert decision module
• HRRP-based target recognition