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<p>The use of navigation spoofing techniques can be an effective method of managing the operation of black-flying unmanned aerial vehicles (UAVs). However, more and more UAVs are equipped with inertial measurement units, adopt loosely coupled GNSS/INS navigation systems, and utilize innovation detection to effectively suppress the impacts of navigation spoofing interference. To address this technical challenge, an innovative covert spoofing algorithm is proposed in this paper. This algorithm transforms the covert spoofing problem into a single-objective optimization problem with constraints by analyzing the effect of GNSS spoofing on the GNSS/INS navigation system. To address this optimization challenge, the algorithm employs genetic algorithms for the first time in covert spoofing scenarios. By improving the selection, crossover, and mutation processes of the genetic algorithm, the algorithm is able to dynamically adjust the GNSS position spoofing parameters. Simulation results demonstrate that the proposed covert spoofing algorithm is capable of successfully spoofing the target without causing innovation detection alarms. Additionally, it is able to successfully covertly spoof the loosely coupled GNSS/INS navigation system to a specified location.</p>

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Optimization of covert spoofing parameters for loosely coupled GNSS/INS systems based on improved genetic algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月1日

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  • Haoyan Chen,
  • Zhijin Wen,
  • Changbiao Lei

摘要

The use of navigation spoofing techniques can be an effective method of managing the operation of black-flying unmanned aerial vehicles (UAVs). However, more and more UAVs are equipped with inertial measurement units, adopt loosely coupled GNSS/INS navigation systems, and utilize innovation detection to effectively suppress the impacts of navigation spoofing interference. To address this technical challenge, an innovative covert spoofing algorithm is proposed in this paper. This algorithm transforms the covert spoofing problem into a single-objective optimization problem with constraints by analyzing the effect of GNSS spoofing on the GNSS/INS navigation system. To address this optimization challenge, the algorithm employs genetic algorithms for the first time in covert spoofing scenarios. By improving the selection, crossover, and mutation processes of the genetic algorithm, the algorithm is able to dynamically adjust the GNSS position spoofing parameters. Simulation results demonstrate that the proposed covert spoofing algorithm is capable of successfully spoofing the target without causing innovation detection alarms. Additionally, it is able to successfully covertly spoof the loosely coupled GNSS/INS navigation system to a specified location.