Moving Object Detection Using a Parallax Shift Vector Algorithm

Area of Science:

• Astronomy
• Astrophysics
• Planetary Science

Background:

• Current asteroid detection algorithms are limited to linear or mildly curved object motion.
• Space-based sensors in low Earth orbit introduce significant parallax for inner Solar System objects.
• This parallax causes highly non-linear motion patterns, challenging traditional detection methods.

Purpose of the Study:

• To adapt existing asteroid detection techniques for non-linear motion.
• To introduce a novel shift vector generation methodology.
• To analyze the impact of this methodology on detection algorithms and processing.

Main Methods:

• Adapting classical pattern matching, matched filtering, and synthetic tracking techniques.
• Developing a new shift vector generation methodology.
• Evaluating impacts on detection algorithms, processing load, and track reporting responsiveness.

Main Results:

• Demonstrated adaptation of matched filtering and synthetic tracking for complex parallax motion.
• Introduced a new shift vector generation method.
• Analyzed the effects on processing load and reporting responsiveness.

Conclusions:

• The proposed methods effectively handle non-linear asteroid motion from space-based parallax.
• The new methodology enhances asteroid detection capabilities for space-based observatories.
• Source code for key components is publicly available on GitHub.