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Optimizing the angle of arrival positioning error of a UAV via an improved nutcracker optimization algorithm.

Yinglei Li1, Qingping Hu2, Shiyan Sun1

  • 1College of Weaponry Engineering, Naval University of Engineering, 717 Jiefang Road, Qiaokou District, Wuhan, 430030, China.

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Summary
This summary is machine-generated.

This study introduces an improved Starling optimization algorithm for drone angle-of-arrival (AOA) positioning, significantly reducing errors. The method enhances positioning accuracy for unmanned aerial vehicles (UAVs) by addressing nonlinear error accumulation.

Keywords:
Airborne optoelectronic podAngle of arrivalError analysisTarget passive localization

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Area of Science:

  • Aerospace Engineering
  • Signal Processing
  • Optimization Algorithms

Background:

  • Traditional drone angle-of-arrival (AOA) positioning suffers from nonlinear error accumulation, reducing accuracy.
  • Existing optimization algorithms often get trapped in local optima, hindering precise positioning.
  • Unmanned aerial vehicle (UAV) passive positioning requires robust methods to overcome these limitations.

Purpose of the Study:

  • To develop a novel positioning error optimization method for UAVs.
  • To enhance the accuracy of AOA positioning by mitigating nonlinear error accumulation.
  • To improve upon existing optimization algorithms by preventing local optima convergence.

Main Methods:

  • Constructed a multi-source error propagation model to analyze UAV position, attitude, and pod attitude errors.
  • Designed a phased optimization framework using observation sequences to suppress nonlinear error accumulation.
  • Integrated an improved Starling optimization algorithm with cubic chaotic mapping and a spiral search strategy for error source compensation.

Main Results:

  • The improved algorithm achieved a 73.29% reduction in positioning error distance compared to traditional AOA positioning.
  • Demonstrated a 58.12% improvement over the original Starling optimization algorithm in simulations.
  • Significantly outperformed other comparison algorithms in correcting nonlinear perturbations.

Conclusions:

  • The proposed method effectively corrects nonlinear perturbations in electro-optical systems for UAVs.
  • This approach provides a higher-precision solution for passive positioning of UAVs.
  • The integration of phased correction and improved Starling optimization offers a robust positioning strategy.