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Cited by 6Indoor and Outdoor Localization TechnologiesTarget Tracking and Data Fusion in Sensor NetworksRobotic Path Planning Algorithms

Node Selection and Path Optimization for Passive Target Localization via UAVs

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Xing, Xiaoyou, Zhong, Zhiwen, Li, Xueting, Yue, Yiyang·2025·Sensors
DOI 10.3390/s25030780PMC PMC11820864
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Abstract

The performance of passive target localization is affected by the positions of unmanned aerial vehicles (UAVs) at a large scale. In this paper, to improve resource utilization efficiency and localization accuracy, the node selection problem and the path optimization problem are jointly investigated. Firstly, the target passive localization model is established and the Chan-based time difference of arrival (TDOA) localization method is introduced. Then, the Cramer–Rao lower bound (CRLB) for Chan-TDOA localization is derived, and the problems of node selection and path optimization are formulated. Secondly, a CRLB-based node selection method is proposed to properly divide the UAVs into several groups, localizing different targets, and a CRLB-based path optimization method is proposed to search for the optimal UAV position configuration at each time step. The proposed path optimization method also effectively handles no-fly-zone (NFZ) constraints, ensuring operational safety while maintaining optimal target tracking performance. Also, to improve the efficiency of path optimization, particle swarm algorithm (PSO) is applied to accelerate the searching process. Finally, numerical simulations are performed to verify the validity and effectiveness of the proposed methods in this paper.

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  • The positioning accuracy of TDOA is closely related to the time delay difference, array error, and array deployment mode.

    “The positioning accuracy of TDOA is closely related to the time delay difference, array error, and array deployment mode [3], so the time delay estimation error has received extensive attention.”

    From Research on the Improvement of the Signal Time Delay Estimation Method of Acoustic Positioning for Anti-Low Altitude UAVs (2025) · cites this paper as [3]

    Section 2.1 models errors in measured time differences, and Section 2.2 links those measurement errors to a bound on localization accuracy. Section 5.3 reports that changing UAV positions improves measurement geometry and localization accuracy. Our copy does not address array-position error as a separate factor, so that part of the claim is unsupported. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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References 27 · Cited by 6

Research works printed but not linked: 25

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Authors
Xing, X., Zhong, Z., Li, X., & Yue, Y.
Affiliations
Sichuan University (CN)
Corresponding author
Li, Xueting
ORCID
Li, Xueting
Venue
Sensors
Publisher
MDPI AG
Published
2025-01-28
Volume
25
Issue
3
Pages
780
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s25030780
PMC
PMC11820864
OpenAlex
W4406909312
Cited by
6
References
27
Citations by year
2026: 3 · 2025: 3
FWCI
1.34
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Qian Yuan Laboratory
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
Keywords
passive target localization, unmanned aerial vehicles, node selection, path optimization, time difference of arrival, Cramer-Rao lower bound, particle swarm optimization, target tracking

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