Implementation of a real-time star centroid extraction algorithm with high speed and superior denoising ability

Abstract

Star tracker is the most precise attitude measuring device, and its advantages include a high resolution and high update rate. Star centroid extraction, which is a very time-consuming process, has great influence on the attitude update rate. This paper proposes a real-time star centroid extraction algorithm based on a field programmable gate array. First, a 1D top-hat filter is used for star segmentation, which is suitable for both uniform and nonuniform background conditions. Second, multichannel image data is reorganized together into a complete frame through image stitching, which prevents the star spots on the channel boundary from being divided into different parts. Finally, star coordinates are extracted by the center-of-mass algorithm. For an image sensor with a resolution of 2048 × 2048 pixels, simulation results conducted by a ModelSim simulator show that the star centroid processing time of a single frame is roughly 5.2 ms. Real night experiments demonstrate that the standard deviation of a star centroid error is within 10 − 2 pixel and the standard deviation of attitude is (2.6 2.2 12.0) arcseconds, which proves that the proposed star centroid extraction algorithm can work continuously and stably.

A High-Accuracy Star Centroid Extraction Method Based on Kalman Filter for Multi-Exposure Imaging Star Sensors

“…At this time, the transmission and processing times of the star image pixel data become a new bottleneck of the AUR. To further improve the AUR, Wang et al [ 33 ] proposed a star centroid extraction method based on distributed parallel super blocks, and Ding et al [ 34 ] also proposed a multichannel star centroid extraction method. The above two methods both utilized the idea of parallel processing to extract the star centroid, thus effectively reducing the pixel data processing time.…”