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Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform.

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  • 1Chinese Academy of Science, Changchun Institute of Optics Fine Mechanics and Physics, Key Laboratory of Airborne Optical Imaging and Measurement, #3888 Dongnanhu Road, Changchun 130033, China. baigb@ciomp.ac.cn.

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Summary
This summary is machine-generated.

This study introduces an improved two-unmanned aerial vehicle (UAV) system for precise localization of moving objects using photoelectric intersection. The enhanced system, incorporating adaptive Kalman filtering, significantly reduces localization errors.

Keywords:
UAV (unmanned aerial vehicles)accuracy analysisadaptive Kalman filteringairborne optoelectronic platformcoordinate transformationintersection localization

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Area of Science:

  • Aerospace Engineering
  • Geomatics Engineering
  • Robotics

Background:

  • Existing unmanned aerial vehicle (UAV) photoelectric localization methods for moving targets have limitations.
  • Accurate real-time localization is crucial for various UAV applications, including surveillance and target tracking.

Purpose of the Study:

  • To propose an improved two-UAV intersection localization system for enhanced accuracy of moving object detection.
  • To analyze the impact of UAV-target geometry on localization precision.
  • To evaluate the effectiveness of modified adaptive Kalman filtering in reducing localization errors.

Main Methods:

  • Development of a two-UAV intersection localization model based on photoelectric theodolite principles.
  • Transformation of line-of-sight (LOS) vectors into homogeneous coordinates using auxiliary coordinate systems.
  • Detailed analysis of the influence of positional relationships between UAVs and the target on localization accuracy.
  • Application of modified adaptive Kalman filtering to refine localization results.

Main Results:

  • An optimal intersection angle of 72.6318° was identified for maximizing localization accuracy.
  • At the optimal position, the localization root mean square error (RMS) was 25.0235 m for a target 5 km away.
  • Modified adaptive Kalman filtering reduced the localization RMS error to 15.7983 m.
  • Outfield experiments yielded errors: longitude 1.63 × 10^-4 (°), latitude 1.35 × 10^-4 (°), altitude 15.8 m, and error radius 27.6 m.

Conclusions:

  • The proposed two-UAV intersection localization system significantly improves the accuracy of locating moving objects compared to existing methods.
  • Optimal geometric configurations and adaptive Kalman filtering are critical for achieving high-precision UAV-based localization.
  • The system demonstrates practical applicability and effectiveness, validated through outfield experiments.