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Field Application of Global Positioning System01:28

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Near-Field Beamforming Algorithms for UAVs.

Yinan Zhang1, Guangxue Wang1, Shirui Peng1

  • 1Department of Information Countermeasures, Air Force Early Warning Academy, Wuhan 430010, China.

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Summary
This summary is machine-generated.

This study introduces three beamforming algorithms for unmanned aerial vehicle (UAV) arrays to overcome positioning and phase errors. These methods improve near-field beamforming performance despite challenging conditions.

Keywords:
Extended Kalman FilterKalman FilterUAVsUnscented Kalman Filternear-field beamformingphase error compensation

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Aerospace Engineering

Background:

  • Unmanned Aerial Vehicle (UAV) arrays face significant challenges in beamforming due to positioning and signal phase errors, particularly under near-field conditions.
  • Effective beamforming is crucial for various UAV applications, including sensing, communication, and navigation.

Purpose of the Study:

  • To develop and validate novel distributed beamforming algorithms for UAV arrays that mitigate the impact of positioning and signal phase errors.
  • To provide robust solutions for near-field beamforming in UAV systems operating under diverse environmental and navigational data availability.

Main Methods:

  • Analysis of the received signal phase error model for UAV arrays in near-field conditions.
  • Development of three beamforming algorithms: Extended Kalman Filter (EKF)-based (no navigation data), Kalman Filter (KF)-based (with navigation data and Taylor expansion), and Unscented Kalman Filter (UKF)-based (iterative noise estimation).
  • Simulation-based validation of the proposed algorithms to assess their performance in error suppression.

Main Results:

  • The proposed EKF, KF, and UKF-based algorithms effectively suppress the adverse effects of positioning and signal phase errors in near-field UAV array beamforming.
  • The algorithms demonstrate robustness under varying conditions, including the availability or absence of navigation data.
  • Simulations confirm the practical applicability and performance improvements offered by the developed beamforming techniques.

Conclusions:

  • The study successfully presents three advanced distributed beamforming algorithms tailored for UAV arrays.
  • These algorithms offer significant improvements in beamforming accuracy and reliability for near-field applications by effectively handling positioning and phase errors.
  • The findings provide valuable insights and a practical reference for implementing UAV array beamforming in real-world scenarios.