Fast Star Matching Method Based on Double K-Vector Lookup Tables for Multi-Exposure Star Trackers

Area of Science:

• Aerospace Engineering
• Computer Vision
• Navigation Systems

Background:

• High update rates are crucial for star tracker performance.
• Multi-Exposure Imaging Approach (MEIA) enhances attitude update rates but faces challenges with increased matching times.
• Existing star matching methods become inefficient with higher multi-exposure times (N) or more navigation stars (M).

Purpose of the Study:

• To develop a fast star matching method for MEIA to overcome performance limitations.
• To accelerate the attitude determination process in star trackers utilizing MEIA.

Main Methods:

• Proposed a novel fast star matching method utilizing double K-vector lookup tables (DKVLUTs).
• Constructed DKVLUTs using information from all predicted stars for efficient lookup.
• Validated the method through both computer simulations and experimental tests.

Main Results:

• The proposed DKVLUT-based method significantly reduces star matching time.
• Matching time was reduced by approximately one order of magnitude compared to existing methods.
• Demonstrated substantial improvements in the efficiency of the MEIA.

Conclusions:

• The DKVLUT method effectively addresses the time-consuming nature of star matching in MEIA.
• This approach enhances the overall performance and feasibility of high-update-rate star trackers.
• The proposed method offers a practical solution for real-time attitude determination.