IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0333009.html
   My bibliography  Save this article

Robust unmanned aerial vehicles tracking amid electronic interference utilizing auxiliary particle filtering

Author

Listed:
  • Le Qi
  • Tao Zhang
  • Guoming Chen
  • Wanyang Wang

Abstract

Electronic interference poses a significant challenge to Unmanned Aerial Vehicle (UAV) tracking systems, compromising navigation accuracy and operational safety in critical applications such as surveillance, disaster response, and infrastructure inspection. This study introduces a novel application of the Auxiliary Particle Filter (APF) for robust UAV tracking under interference conditions, focusing on fixed-reference scenarios. The APF incorporates adaptive proposal distributions and robust weight updates to effectively mitigate interference-induced measurement errors. Through comprehensive simulation that evaluates performance under varying interference and sensor degradation scenarios, the APF demonstrates superior accuracy, achieving a mean Root Mean Square Error (RMSE) of 4.82 meters with low variability (σ=0.30m). This significantly outperforms traditional filters, including the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Notably, the APF maintains stable performance even under severe interference conditions, where conventional approaches exhibit substantial degradation. Statistical validation confirms these improvements across all test scenarios (p

Suggested Citation

  • Le Qi & Tao Zhang & Guoming Chen & Wanyang Wang, 2025. "Robust unmanned aerial vehicles tracking amid electronic interference utilizing auxiliary particle filtering," PLOS ONE, Public Library of Science, vol. 20(9), pages 1-22, September.
    • Handle: RePEc:plo:pone00:0333009
    DOI: 10.1371/journal.pone.0333009
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0333009
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0333009&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0333009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zongru Liu & Jiyu Li, 2023. "Application of Unmanned Aerial Vehicles in Precision Agriculture," Agriculture, MDPI, vol. 13(7), pages 1-4, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zenan Shen & Shaoquan Liu & Wei Zhu & Daoyuan Ren & Qiang Xu & Yu Feng, 2024. "A Review on Key Technologies and Developments of Hydrogen Fuel Cell Multi-Rotor Drones," Energies, MDPI, vol. 17(16), pages 1-36, August.
    2. Michail Semenišin & Dainius Steponavičius & Aurelija Kemzūraitė & Dainius Savickas, 2025. "Optimizing UAV Spraying for Sustainability: Different System Spray Drift Control and Adjuvant Performance," Sustainability, MDPI, vol. 17(5), pages 1-26, February.
    3. Hasan Raza Khanzada & Adnan Maqsood & Abdul Basit, 2025. "Reinforcement learning for UAV flight controls: Evaluating continuous space reinforcement learning algorithms for fixed-wing UAVs," PLOS ONE, Public Library of Science, vol. 20(10), pages 1-39, October.
    4. Adisak Suvittawat, 2024. "Investigating Farmers’ Perceptions of Drone Technology in Thailand: Exploring Expectations, Product Quality, Perceived Value, and Adoption in Agriculture," Agriculture, MDPI, vol. 14(12), pages 1-25, November.
    5. Qifeng Niu & Jinhui Han & Zhen Sui & Feng Xu, 2025. "Lightweight deep neural networks: Optimization of vehicle classification using ICBAM based on depthwise separable convolutions," PLOS ONE, Public Library of Science, vol. 20(11), pages 1-33, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0333009. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.