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<p>In recent years, the rapid advancement of Unmanned Aerial Vehicle (UAV) technology has posed significant security risks to critical infrastructure. Particularly in airport environments, unauthorized UAV incursions pose a threat to aviation safety. Consequently, developing effective anti-UAV systems has become an important research topic. Focusing on the key scenario of airports, this paper proposes a deployment model for anti-UAV detection system. The model comprehensively considers the airspace configuration and technical requirements of the airport, aiming to achieve a trade-off between maximizing the early warning detection efficiency and maximizing the coverage strength. To solve this model, a Multi-Objective Evolutionary Algorithm Framework with Problem-Specific Initialization and Offspring Generation Operators (MOEA-IG) is designed, which supports embedding various environmental selection operators. By embedding the four baseline algorithms into the MOEA-IG framework, the Hypervolume (HV) metric demonstrates a 34.9–59.7% improvement, and the running time is also significantly reduced. Experimental results confirm that the deployment model for anti-UAV detection system is consistent with reality, and the MOEA-IG can effectively solve this model. This research provides a scientific and efficient decision-making basis for the optimal deployment of anti-UAV detection systems in complex airport environments, contributing to enhancing the security defense capability against unauthorized UAV intrusions and ensuring the safe operation of critical air transportation infrastructure.</p>

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Multi-objective deployment planning of anti-UAV detection system at airport

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Tonglin Liu,
  • Jinggai Geng,
  • Xin Sun,
  • Xinhui Si,
  • Hu Zhang,
  • Yulan Lu

摘要

In recent years, the rapid advancement of Unmanned Aerial Vehicle (UAV) technology has posed significant security risks to critical infrastructure. Particularly in airport environments, unauthorized UAV incursions pose a threat to aviation safety. Consequently, developing effective anti-UAV systems has become an important research topic. Focusing on the key scenario of airports, this paper proposes a deployment model for anti-UAV detection system. The model comprehensively considers the airspace configuration and technical requirements of the airport, aiming to achieve a trade-off between maximizing the early warning detection efficiency and maximizing the coverage strength. To solve this model, a Multi-Objective Evolutionary Algorithm Framework with Problem-Specific Initialization and Offspring Generation Operators (MOEA-IG) is designed, which supports embedding various environmental selection operators. By embedding the four baseline algorithms into the MOEA-IG framework, the Hypervolume (HV) metric demonstrates a 34.9–59.7% improvement, and the running time is also significantly reduced. Experimental results confirm that the deployment model for anti-UAV detection system is consistent with reality, and the MOEA-IG can effectively solve this model. This research provides a scientific and efficient decision-making basis for the optimal deployment of anti-UAV detection systems in complex airport environments, contributing to enhancing the security defense capability against unauthorized UAV intrusions and ensuring the safe operation of critical air transportation infrastructure.