A Centroiding Method for Targets in Star Images Based on Local PSF Estimation and Iterative Deconvolution

During astronomical observation, the centroiding of space targets is a crucial technology for improving trajectory association in images, celestial positioning, and orbit determination. However, when the exposure time is short, the distribution of targets and stars in different regions of an image can vary significantly, seriously affecting centroiding accuracy. Motivated by the relationship between target distribution and the corresponding optical system, we propose a centroiding method based on local point spread function (PSF) estimation and iterative deconvolution. First, the imaging process of an ideal target is analyzed to identify key factors affecting the distribution of various targets. Next, the local PSF around a target is estimated using the distribution of nearby stars. Then, iterative deconvolution is used to reconstruct the region of interest (ROI) around the target with the estimated local PSF. Finally, an improved weighted centroid is calculated using a restricted region growing method. The proposed method was tested on simulation experiments and multiple sequences of collected real optical images with stars and targets in different regions. The results showed outstanding performance in terms of mean absolute errors, standard deviation of star location, and movement fluctuations compared to baseline methods. The mean absolute errors for centroiding in Gaussian noise with a standard deviation of less than 0.02 and displacement within 1 pixel are 0.16 and 0.06 pixels, respectively.