Improved space object detection using short-exposure image data with daylight background.

Area of Science:

• Astronomy and Astrophysics
• Space Situational Awareness
• Image Processing

Background:

• Space object detection is vital for space situational awareness.
• Current methods often rely on long-exposure imaging, which is challenging under daylight conditions.
• Traditional algorithms assume Gaussian data distribution and use matched filters or spatial correlators.

Purpose of the Study:

• To enhance the detection capabilities for small and dim space objects.
• To improve upon traditional stack-and-average methods for creating long-exposure images from short exposures.
• To increase the signal-to-noise ratio in stacked images against bright backgrounds.

Main Methods:

• A novel algorithm is proposed that selectively removes short-exposure frames not contributing to the signal-to-noise ratio.
• The algorithm refines the traditional stack-and-average technique for image stacking.
• Performance was evaluated using MATLAB-generated data and laboratory-collected data.

Main Results:

• The proposed algorithm demonstrated improved detection capabilities compared to traditional matched filter detectors.
• Receiver operating characteristic (ROC) curves illustrated a higher probability of detection.
• Selective frame removal effectively enhanced the signal-to-noise ratio of the averaged image.

Conclusions:

• The developed algorithm offers a significant improvement for detecting faint space objects in challenging conditions.
• Selective frame averaging is a viable strategy to boost detection performance in space situational awareness.
• This method holds promise for more effective cataloging and tracking of space objects.