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UAV Path Optimization for Angle-Only Self-Localization and Target Tracking Based on the Bayesian Fisher Information

Kutluyil Dogancay¹, Hatem Hmam²

¹UniSA STEM, University of South Australia, Mawson Lakes Campus, Mawson Lakes, SA 5095, Australia.

Sensors (Basel, Switzerland)

|May 25, 2024

Summary

New algorithms optimize uncrewed aerial vehicle (UAV) paths for self-localization and target tracking using landmark bearings and angle-of-arrival data. This approach enhances navigation accuracy when Global Navigation Satellite System (GNSS) is unavailable.

Keywords:

A-optimality criterion Bayesian Fisher information matrix D-optimality criterion Kalman filter autonomous vehicles self-localization target tracking

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Area of Science:

Robotics and Control Systems
Navigation and Guidance
Signal Processing

Background:

Global Navigation Satellite System (GNSS) unreliability necessitates alternative methods for uncrewed aerial vehicle (UAV) navigation.
Accurate self-localization and target tracking are crucial for UAV missions, especially in GNSS-denied environments.
Orientation estimation is vital for precise UAV self-localization, as errors can significantly degrade performance.

Purpose of the Study:

Develop novel path optimization algorithms for UAV self-localization and target tracking.
Integrate beacon bearings and angle-of-arrival (AOA) measurements for enhanced navigation.
Jointly estimate UAV orientation and location for improved accuracy.

Main Methods:

Formulated the joint self-localization and target tracking as a Kalman filtering problem with an augmented state vector.
Utilized beacon bearings and target AOA measurements within the Kalman filter framework.
Employed Bayesian Fisher information matrix optimization (A- and D-optimality criteria) for path planning.
Proposed a modified closed-form projection algorithm for optimal UAV path determination.

Main Results:

Developed and validated new UAV path optimization algorithms through extensive simulations.
Demonstrated the effectiveness of joint state estimation (location and orientation) for improved self-localization.
Evaluated algorithm performance across various measurement noise levels.
Showcased the utility of the proposed methods in scenarios lacking GNSS availability.

Conclusions:

The developed algorithms provide robust UAV self-localization and target tracking solutions.
Optimal path planning significantly enhances navigation accuracy, particularly in GNSS-challenged environments.
The joint estimation approach effectively mitigates orientation errors, boosting overall localization performance.