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Wang Yuming, Ma Liyun, Chen Yazhou. Electromagnetic protection design for drones based on biomimetic mapping[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202537.250277
Citation: Wang Yuming, Ma Liyun, Chen Yazhou. Electromagnetic protection design for drones based on biomimetic mapping[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202537.250277

Electromagnetic protection design for drones based on biomimetic mapping

doi: 10.11884/HPLPB202537.250277
  • Received Date: 2025-08-30
  • Accepted Date: 2025-09-22
  • Rev Recd Date: 2025-10-10
  • Available Online: 2025-10-17
  • Background
    Human-represented biological intelligence is transplantable into general intelligence across four levels, namely data intelligence, perceptual intelligence, cognitive intelligence, and autonomous intelligence. As intelligent unmanned technologies advance, electronic systems undergo evolution toward higher intelligence and autonomy; concurrently, bionic electromagnetic protection—an approach that enhances compatibility, adaptability, and threat resistance—evolves in tandem with these electronic systems. Nevertheless, significant gaps persist in the translation of biological mechanisms into practical applications for electronic systems.
    Purpose
    Focused on drones as the research subject, this study explores the implementation of biomimetic mapping for drone electromagnetic protection across the aforementioned four levels, with the exploration grounded in core drone performances including communication, navigation, detection, and control. The primary objective of this study is to advance the development of electromagnetic protection toward higher levels of intelligence.
    Methods
    Guided by fundamental design principles of structure-function integration and multi-level immune protection, the research categorizes biomimetic mapping into information and signal levels: the information level encompasses spectrum sensing and autonomous decision-making, while the signal level concentrates on limiting and filtering technologies. For key drone systems, an intelligent data link was designed using software-defined radio technology; navigation receivers were optimized through the integration of shielding, filtering, and anti-interference antennas; a GAN-based image self-repair algorithm incorporating hybrid attention was proposed; and the application of neuromorphic circuits and bionic structures for control systems was explored.
    Results
    The study successfully developed a spectrum-sensing adaptive data link and a reinforced navigation receiver, and verified the initial realization of perceptual intelligence in selected system components.
    Conclusions
    Biomimetic mapping contributes to the enhancement of drone electromagnetic protection; however, challenges remain, including the refinement of mapping methods, the development of novel devices, and the advancement of intelligent computing. Future research efforts directed at addressing these challenges will facilitate the full realization of autonomous intelligence in drone electromagnetic protection systems.
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