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Related Experiment Video

Updated: Aug 27, 2025

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

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Autonomous Navigation of Unmanned Aircraft Using Space Target LOS Measurements and QLEKF.

Kai Xiong¹, Peng Zhou¹, Chunling Wei¹

¹Science and Technology on Space Intelligent Control Laboratory, Beijing Institute of Control Engineering, Beijing 100094, China.

Sensors (Basel, Switzerland)

|September 23, 2022

Summary

This summary is machine-generated.

This study introduces an autonomous navigation method for unmanned aircraft, fusing inertial navigation system (INS) data with star observations. This approach enhances navigation accuracy, especially in GPS-denied environments.

Keywords:

LOS measurement Q-learning autonomous navigation extended Kalman filter unmanned aircraft

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Area of Science:

Aerospace Engineering
Navigation Systems
Robotics

Background:

Conventional INS/GNSS navigation degrades in GPS-denied environments.
INS/CNS navigation offers a supplement but struggles with INS position error suppression.
Autonomous navigation for unmanned aircraft requires robust solutions beyond GNSS.

Purpose of the Study:

To develop a novel autonomous navigation method for unmanned aircraft.
To improve INS accuracy using celestial observations and advanced filtering.
To address limitations of existing INS/CNS integrated navigation systems.

Main Methods:

Fusion of Inertial Navigation System (INS) measurements with line-of-sight (LOS) observations of space targets.
Utilizing a star camera to observe space targets for correcting INS position, velocity, and attitude errors.
Designing a Q-learning extended Kalman filter (QLEKF) for enhanced navigation performance.

Main Results:

Demonstrated effectiveness of the autonomous navigation method through Cramer-Rao lower bounds (CRLB) analysis.
Numerical simulations confirmed the performance enhancement of the integrated navigation system.
Successful correction of INS errors using star camera observations.

Conclusions:

The proposed INS/star camera integrated navigation method provides a viable solution for autonomous navigation.
The QLEKF significantly enhances the performance of the integrated navigation system.
This approach offers improved navigation accuracy and robustness in GNSS-denied scenarios.