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Node Selection and Path Optimization for Passive Target Localization via UAVs.

Xiaoyou Xing1, Zhiwen Zhong1, Xueting Li1

  • 1School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China.

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|February 13, 2025
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Summary
This summary is machine-generated.

This study optimizes unmanned aerial vehicle (UAV) networks for passive target localization. It jointly addresses node selection and path optimization using the Cramer-Rao lower bound (CRLB) for improved accuracy and efficiency.

Keywords:
Cramer–Rao lower bound (CRLB)node selection methodparticle swarm algorithm (PSO)passive target localizationpath optimization methodunmanned aerial vehicles (UAVs)

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Area of Science:

  • Robotics and Control Systems
  • Signal Processing and Communications
  • Aerospace Engineering

Background:

  • Passive target localization performance is significantly influenced by the spatial configuration of unmanned aerial vehicles (UAVs).
  • Existing methods often struggle with optimizing resource utilization and localization accuracy simultaneously in large-scale UAV networks.

Purpose of the Study:

  • To enhance resource utilization efficiency and localization accuracy in large-scale passive target localization scenarios.
  • To jointly investigate and solve the node selection and path optimization problems for UAV networks.

Main Methods:

  • Established a passive target localization model and introduced the Chan-based time difference of arrival (TDOA) method.
  • Derived the Cramer-Rao lower bound (CRLB) for Chan-TDOA localization to formulate node selection and path optimization.
  • Developed CRLB-based node selection and path optimization methods, incorporating no-fly-zone (NFZ) constraints and utilizing the particle swarm optimization (PSO) algorithm.

Main Results:

  • Proposed a CRLB-based node selection strategy to efficiently group UAVs for localizing different targets.
  • Introduced a CRLB-based path optimization method that finds optimal UAV configurations while respecting NFZ constraints.
  • Demonstrated through numerical simulations that the proposed methods effectively improve localization accuracy and resource utilization.

Conclusions:

  • The joint optimization of node selection and path planning significantly enhances passive target localization performance in UAV networks.
  • The proposed CRLB-based methods provide a robust framework for efficient and accurate target tracking under operational constraints.
  • The integration of PSO accelerates the optimization process, making the solution practical for real-time applications.