Reverse Procedure Detection of Space Target Streaks Based on Motion Parameter Estimation

Abstract

This paper proposes a reverse procedure detection method for space target streaks based on motion parameter estimation. According to the phase shift characteristics of the spectrum, the interframe phase difference is used to obtain the displacement vectors of the target streak and image background, and then the morphological parameters of the target streak are estimated. In addition, a streak window is designed on the basis of the parameters, utilizing the local grayscale correlation in two image frames to carry out target searching and positioning with the interval of the target displacement vector. Images from different scenes are used to evaluate the adaptability of our method, and the experimental results suggest that the expected targets can be detected successfully with subpixel precision in all telescope operating modes. The proposed frequency-domain reverse procedure detection method avoids the problem of fake target confirmation caused by blind detection in traditional methods and overcomes the interference produced when the target streak passes through a bright star. Moreover, first target tracking and then positioning, which cannot be performed with time-domain methods, can be achieved. Our proposed method is attractive for multiple applications, such as space target cataloging, early warning and orbit prediction.INDEX TERMS Reverse procedure detection, motion parameter estimation, morphological parameter estimation, local correlation positioning.

Infrared Dim Star Background Suppression Method Based on Recursive Moving Target Indication

“…Space infrared remote sensing provides full-time and full-weather observation of objects, making it the main tool for surveilling space targets. However, due to the long detection range, limited resolution, low radiant energy, and small target size, these space targets appear as dim point targets in the focal plane [5][6][7][8][9]. These low signal-to-noise ratio (SNR) point targets are inherently difficult to detect, especially with complex backgrounds and noise [10][11][12][13][14].…”