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Trajectory Optimization to Enhance Observability for Bearing-Only Target Localization and Sensor Bias Calibration.

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Summary

This study enhances unmanned aerial vehicle (UAV) target localization using a control barrier function (CBF) for motion planning. The new method significantly improves observability and localization accuracy, outperforming existing algorithms.

Keywords:
bio-inspirationcontrol barrier functionobservability enhancementtarget localizationtrajectory optimization

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Area of Science:

  • Robotics and Control Systems
  • Navigation and Guidance
  • Optimization Algorithms

Background:

  • Bearing-only target localization is challenging due to sensor bias.
  • Existing observability analysis methods (e.g., rank criterion) offer limited quantitative insights.
  • Unmanned Aerial Vehicle (UAV) motion planning is crucial for improving localization performance.

Purpose of the Study:

  • To develop a novel method for enhancing observability in bearing-only target localization for UAVs.
  • To address sensor bias contamination in localization systems.
  • To optimize UAV trajectories for improved localization accuracy and convergence.

Main Methods:

  • A control barrier function (CBF)-based approach for UAV motion planning, inspired by plant phototropism.
  • Quantitative observability analysis using the condition number to identify key influencing factors.
  • Formulation and solution of a multi-objective, nonlinear optimization problem using the Nonlinear Constrained Multi-Objective Gray Wolf Optimization Algorithm (NCMOGWOA).

Main Results:

  • A threefold reduction in the condition number, significantly enhancing system observability.
  • The proposed NCMOGWOA algorithm demonstrated superior performance in localization accuracy and convergence.
  • Achieved lowest Generational Distance (GD) of 7.3442 and Inverted Generational Distance (IGD) of 8.4577 compared to other algorithms.
  • Explored the impact of CBF attenuation rates and initial flight path angles on system performance.

Conclusions:

  • The proposed CBF-based motion planning method effectively enhances observability for bearing-only target localization.
  • The NCMOGWOA is a robust and efficient algorithm for optimizing UAV trajectories in complex scenarios.
  • The study provides a significant advancement in autonomous navigation and target tracking systems.