Plume Noise Suppression Algorithm for Missile-Borne Star Sensor Based on Star Point Shape and Angular Distance...

Area of Science:

• Aerospace Engineering
• Computer Vision
• Signal Processing

Background:

• Missile launches generate plume noise, creating artificial stars in star images.
• This noise interferes with the normal functioning of missile-borne star sensors.

Purpose of the Study:

• To develop a robust plume noise suppression algorithm for star identification.
• To improve the accuracy and reliability of star sensors in the presence of significant noise.

Main Methods:

• Utilizing Principal Component Analysis (PCA) for star point shape feature extraction.
• Evaluating star point authenticity using length-width ratios.
• Employing shape features to determine optimal matching windows for star point correlation across frames.
• Applying the principle of invariant angular distance for rapid elimination of false stars.

Main Results:

• The proposed algorithm demonstrates high robustness and speed in eliminating fake stars.
• Effective noise suppression is achieved even when fake stars outnumber true stars by four to one.
• The algorithm significantly outperforms existing methods in scenarios with high fake star density.

Conclusions:

• The developed algorithm is effective in suppressing plume noise and identifying true stars.
• It meets the stringent requirements of spacecraft systems, even under severe noise conditions.
• This preprocessing technique enhances the performance of star identification systems.