Autonomous space target recognition and tracking approach using star sensors based on a Kalman filter

Area of Science:

• Space exploration and astronomy
• Astrodynamics and orbital mechanics
• Computer vision and signal processing

Background:

• Space targets like satellites and debris share similar characteristics with background stars in 2D images.
• Traditional tracking methods struggle to differentiate between targets and stars due to similar point-spread functions and apparent motion.
• This limitation hinders autonomous recognition and tracking of space objects.

Purpose of the Study:

• To develop an autonomous approach for recognizing and tracking space targets.
• To overcome the limitations of traditional methods in distinguishing targets from stars.
• To enable reliable identification and monitoring of satellites and debris in orbit.

Main Methods:

• A novel two-step method for subpixel-scale detection of celestial objects, including both stars and targets.
• Integration of a star sensor technique for enhanced object detection and characterization.
• Application of a Kalman filter (KF) for robust tracking of identified space targets.

Main Results:

• The proposed method successfully achieved subpixel-scale detection of star objects.
• The combined star sensor technique and Kalman filter demonstrated effective target tracking.
• Experimental results validated the system's capability for autonomous space target recognition.

Conclusions:

• The developed autonomous space target recognition and tracking approach is effective.
• The method successfully distinguishes between space targets and background stars.
• This technique offers a viable solution for autonomous monitoring of space objects.