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Iterative localization method for multiple jammers in UAV collaborative jamming attacks.

Li Huang1, Luhao Xiong2, Shijie Huang1

  • 1School of Cyber Science and Engineering, Southeast University, Nanjing, 210000, China.

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Summary
This summary is machine-generated.

This study introduces a new algorithm for locating multiple jammers in drone attacks. The Multi-Strategy Improved Grey Wolf Optimizer (MSIGWO) enhances localization accuracy and jammer identification.

Keywords:
Grey wolf optimizer algorithmJammer localizationJamming attackUAV network

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Area of Science:

  • * Electrical Engineering and Computer Science
  • * Robotics and Autonomous Systems

Background:

  • * Collaborative jamming attacks by multiple Unmanned Aerial Vehicles (UAVs) pose significant threats to communication networks.
  • * Accurate localization of these jammers is crucial for effective countermeasures and network security.

Purpose of the Study:

  • * To develop a robust algorithm for multi-jammer localization in UAV-based collaborative jamming scenarios.
  • * To enhance the accuracy and efficiency of jammer localization through advanced optimization techniques.

Main Methods:

  • * Introduction of network, jamming, and node models specific to multi-jammer scenarios.
  • * Development of an error minimization-based optimization method, transforming localization into a single-objective problem.
  • * Proposal of a Multi-Strategy Improved Grey Wolf Optimizer (MSIGWO) incorporating nonlinear convergence factors, adaptive differential mutation, and chaotic local search.

Main Results:

  • * The proposed Multi-Strategy Improved GWO-based and Error Minimization Multi-Jammer Iterative Localization Algorithm (MSIGWO-MJL) demonstrates high localization accuracy.
  • * The algorithm accurately estimates the number of jammers present in the attack scenario.
  • * Experimental results confirm excellent overall performance in complex multi-jammer attack environments.

Conclusions:

  • * The MSIGWO-MJL algorithm offers a significant advancement in addressing multi-jammer localization challenges.
  • * The proposed optimization strategies effectively improve convergence speed and prevent local optima.
  • * This research provides a valuable tool for enhancing the resilience of UAV systems against jamming attacks.