JointHRRP-Net: A Statistically Constrained Decoupling Network for Joint Target and Jamming Recognition in Composite Jamming
arXiv:2605.22857v1 Announce Type: cross 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 targe
The increasing sophistication of electronic warfare and counter-measures necessitates advanced AI solutions to maintain military advantage and operational clarity. This research addresses a critical gap in radar performance against composite jamming.
Advanced target recognition in jammed environments is crucial for modern military operations, impacting situational awareness, targeting accuracy, and overall defence capabilities. This AI-driven solution enhances sensor reliability in contested spaces.
Radar systems can become more resilient to composite jamming, allowing for clearer identification of targets even when faced with sophisticated electronic countermeasures. This reduces the effectiveness of an adversary's jamming techniques.
- · Defence contractors
- · Military forces relying on radar
- · AI/ML research institutions
- · Adversaries relying on electronic jamming
- · Outdated radar systems
Improved performance of radar systems in contested electromagnetic environments, leading to enhanced target identification.
Accelerated development and adoption of AI-driven electronic warfare counter-countermeasures across military platforms.
A potential arms race in AI-enabled electronic warfare capabilities, requiring continuous innovation in both jamming and anti-jamming technologies.
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