An anti-detection flight method for drone swarm based on photosensitive sensing

Lei Yilin; Li Yudong; Chen Cheng; Hu Tianjiang

doi:10.11714/acta.snus.ZR20260065

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An anti-detection flight method for drone swarm based on photosensitive sensing

Research articles | 更新时间：2026-10-20

- An anti-detection flight method for drone swarm based on photosensitive sensing
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 65, Issue 5, Pages: 109-117(2026)
- 作者机构：
  
  1.中山大学航空航天学院，广东深圳 518107
  2.中山大学人工智能学院，广东珠海 519080
  3.珠海市群体智能与无人系统重点实验室，广东珠海 519080
- 作者简介：
- 基金信息：
- DOI：10.11714/acta.snus.ZR20260065
  CLC： V279
- Received：19 March 2026，
  
  Revised：2026-05-07，
  
  Accepted：07 May 2026，
  
  Online First：29 June 2026，
  
  Published：25 September 2026
- 稿件说明：
移动端阅览
雷一林,李裕东,陈程等.基于光敏感知的无人机集群反探测飞行方法[J].中山大学学报(自然科学版)(中英文),2026,65(05):109-117.

Lei Yilin,Li Yudong,Chen Cheng,et al.An anti-detection flight method for drone swarm based on photosensitive sensing[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2026,65(05):109-117.
雷一林,李裕东,陈程等.基于光敏感知的无人机集群反探测飞行方法[J].中山大学学报(自然科学版)(中英文),2026,65(05):109-117. DOI： 10.11714/acta.snus.ZR20260065.

Lei Yilin,Li Yudong,Chen Cheng,et al.An anti-detection flight method for drone swarm based on photosensitive sensing[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2026,65(05):109-117. DOI： 10.11714/acta.snus.ZR20260065.

摘要

针对现有研究侧重于对无人机外部环境与目标的感知，而缺乏被外部主动探测的机载感知能力的问题，提出一种基于光敏感知的无人机集群反探测飞行方法。首先，设计轻量化光敏感知模组，获取无人机周围局部方向性光强观测信息；在此基础上，设计融合瞬时观测与历史权重的反探测规避策略，并结合反探测风险传播模型实现局部规避信息在集群中的传播与协同响应；同时，构建了用于局部方向性光强观测的机载光敏实验平台。验证实验结果表明：在单机场景下，相较于无规避策略，所提方法使无人机在威胁区域内的平均暴露时间降低了27.90%，规避过程中航向角的最大调整为46.80°；在集群协同场景下，集群平均暴露时间较无反探测策略降低了26.62%，较传统速度平均策略降低了9.39%。研究成果为无人机集群反探测协同飞行提供了可借鉴的实验数据与理论方法。

Abstract

Existing studies mostly focus on sensing external environments and targets by drones， with limited attention to onboard perception of active external detection. To solve this problem， this paper proposes a photosensitive sensing anti-detection flight method for drone swarms. First， we develop a compact photosensitive sensor unit that measures ambient directional illumination at each point in space around the drone. Based on the above， we devise an anti-detection evasion strategy by combining online observation and history weighting， and couple it with a risk propagation anti-detection model to enable local evasion knowledge sharing and coordination within the swarm. In addition， an onboard photosensitive experimental platform is constructed for local directional light-intensity observation. Experimental results show that， in the single-drone scenario， compared with no avoidance strategy， the proposed approach reduces the mean exposure time inside the threat area by 27.90%， with a maximum heading change of 46.80° during avoidance. For the swarm-cooperative case， our approach can reduce the average swarm exposure time by 26.62% compared with the strategy without anti-detection， and by 9.39% compared with the traditional velocity averaging strategy. The results provide a useful reference platform， experimental data， and theoretical support for coordinated anti-detection flight in drone swarms.

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references

王建，韦卓，辛红强，等， 2025 . 典型场景下的无人机探测反制技术及应用［J］. 兵器装备工程学报， 46 （ 2 ）： 72 - 79 .

晏青，熊峻江，游思明， 2011 . 基于动态RCS的无人机航迹实时规划［J］. 北京航空航天大学学报， 37 （ 9 ）： 1115 - 1121 .

张欣睿，时晨光，吴志锋，等， 2025 . 动态威胁下基于改进 APF-RRT* 算法的无人机集群隐身航迹规划算法［J］. 电子与信息学报， 47 （ 12 ）： 5178 - 5191 .

Balázs B ， Vásárhelyi G ， Vicsek T ， 2020 . Adaptive leadership overcomes persistence-responsivity trade-off in flocking ［J］. J R Soc Interface ， 17 （ 167 ）： 20190853 .

Chakraborty D ， Bhunia-Rumi De S ， 2020 . Survival chances of a prey swarm：How the cooperative interaction range affects the outcome ［J］. Sci Rep ， 10 ： 8362 .

Chen Z X ， Luo Z H ， Jin X J ， et al ， 2025 . Multi-UAV path planning problem with biased sampling，candidate evaluation，and path reconfiguration in complex environment with threats ［J］. Expert Syst Appl ， 291 ： 128558 .

Han Z L ， Chen M ， Zhu H J ， et al ， 2024 . Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm ［J］. Def Technol ， 32 ： 1 - 22 .

Hauert S ， Leven S ， Varga M ， et al ， 2011 . Reynolds flocking in reality with fixed-wing robots： Communication range vs maximum turning rate ［C］// IEEE/RSJ International Conference on Intelligent Robots and Systems . San Francisco， CA， USA ： 5015 - 5020 .

Huang J L ， Wang F K ， Hu T J ， 2023 . CoFlyers： A universal platform for collective flying of swarm drones ［C］// IEEE/RSJ International Conference on Intelligent Robots and Systems . Detroit， MI， USA ： 8808 - 8813 .

Huang J L ， Zhu B ， Hu T J ， 2024 . ATI： Assemble topological interaction overcomes consistency-cohesion trade-off in bird flocking ［J］. IET Cyber Syst Robot ， 6 （ 2 ）： 12114 .

Jiang W ， Lyu Y X ， Li Y F ， et al ， 2022 . UAV path planning and collision avoidance in 3D environments based on POMPD and improved grey wolf optimizer ［J］. Aerosp Sci Technol ， 121 ： 107314 .

Kahn J M ， Barry J R ， 2002 . Wireless infrared communications ［J］. Proc IEEE ， 85 （ 2 ）： 265 - 298 .

Mezey D ， Bastien R ， Zheng Y ， et al ， 2025 . Purely vision-based collective movement of robots ［J］. NPJ Robot ， 3 （ 1 ）： 11 .

Reynolds C W ， 1987 . Flocks， herds and schools：A distributed behavioral model ［J］. SIGGRAPH Comput Graph ， 21 （ 4 ）： 25 - 34 .

Vásárhelyi G ， Virágh C ， Somorjai G ， et al ， 2018 . Optimized flocking of autonomous drones in confined environments ［J］. Sci Robot ， 3 （ 20 ）： 3536 .

Vicsek T ， Czirók A ， Ben-Jacob E ， et al ， 1995 . Novel type of phase transition in a system of self-driven particles ［J］. Phys Rev Lett ， 75 （ 6 ）： 1226 - 1229 .

Wang F K ， Huang J L ， Low K H ， et al ， 2023 . AGDS： Adaptive goal-directed strategy for swarm drones flying through unknown environments ［J］. Complex Intell Syst ， 9 （ 2 ）： 2065 - 2080 .

Xu Q Q ， Ge J Q ， Yang T ， et al ， 2020 . A trajectory design method for coupling aircraft radar cross-section characteristics ［J］. Aerosp Sci Technol ， 98 ： 105653 .

Yan C ， Xiang X J ， Wang C ， 2020 . Towards real-time path planning through deep reinforcement learning for a UAV in dynamic environments ［J］. J Intell Rob Syst ， 98 （ 2 ）： 297 - 309 .

Zhang Z ， Jiang J ， Wu J ， et al ， 2023 . Efficient and optimal penetration path planning for stealth unmanned aerial vehicle using minimal radar cross-section tactics and modified A-Star algorithm ［J］. ISA Trans ， 134 ： 42 - 57 .

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