A star identification algorithm for rolling shutter exposure based on Hough transform

Abstract

The star identification algorithm usually identifies stars by angular distance matching. However, under high dynamic conditions, the rolling shutter effect distorts the angular distances between the measured and true star positions, leading to plethoric false matches and requiring complex and time-consuming verification for star identification. Low identification rate hinders the application of low-noise and cost-effective rolling shutter image sensors. In this work, we first study a rolling shutter distortion model of angular distances between stars, and then propose a novel three-stage star identification algorithm to identify distorted star images captured by the rolling shutter star sensor. The first stage uses a modified grid algorithm with adaptive error tolerance and an expanded pattern database to efficiently eliminate spurious matches. The second stage performs angular velocity estimation based on Hough transform to verify the matches that follow the same distortion pattern. The third stage applies a rolling shutter error correction method for further verification. Both the simulation and night sky image test demonstrate the effectiveness and efficiency of our algorithm under high dynamic conditions. The accuracy of angular velocity estimation method by Hough transform is evaluated and the root mean square error is below 0.5 (°)/s. Our algorithm achieves a 95.7% identification rate at an angular velocity of 10 (°)/s, which is much higher than traditional algorithms.