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In modern warfare, achieving swift and precise target localization is pivotal for the victory. The geometric arrangement of sensors and targets significantly influences target localization accuracy in a 3D localization system based on Time Difference of Arrival (TDOA). To enhance localization accuracy, this paper introduces a Cramer-Rao Lower Bound (CRLB) derived from the localization error within the target area, using the sum of the CRLB’s trace as the optimization criterion. Concurrently, based on the standard Grey Wolf Optimization (GWO), we adjust the weight function and the update strategy of GWO, and present a novel Improved GWO algorithm. Finally, the Improved GWO algorithm is used to carry out the optimal deployment of the station in the specified region for the simulation study, and simulation results demonstrate that the Improved GWO algorithm effectively enhances the overall localization accuracy of the target area when compared with standard GWO and Particle Swarm Optimization. These findings confirm the effectiveness and superiority of the Improved GWO algorithm.

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Optimal UAVs Placement for TDOA Localization Method Based On An Improved Grey Wolf Optimization Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Yu Cheng Yao,
  • Xie Kai,
  • An Zhi Yin

摘要

In modern warfare, achieving swift and precise target localization is pivotal for the victory. The geometric arrangement of sensors and targets significantly influences target localization accuracy in a 3D localization system based on Time Difference of Arrival (TDOA). To enhance localization accuracy, this paper introduces a Cramer-Rao Lower Bound (CRLB) derived from the localization error within the target area, using the sum of the CRLB’s trace as the optimization criterion. Concurrently, based on the standard Grey Wolf Optimization (GWO), we adjust the weight function and the update strategy of GWO, and present a novel Improved GWO algorithm. Finally, the Improved GWO algorithm is used to carry out the optimal deployment of the station in the specified region for the simulation study, and simulation results demonstrate that the Improved GWO algorithm effectively enhances the overall localization accuracy of the target area when compared with standard GWO and Particle Swarm Optimization. These findings confirm the effectiveness and superiority of the Improved GWO algorithm.