A Dim Space Target Detection and Track Association Method for Dense Stellar Backgrounds.

In space target surveillance missions, effective detection of dim space targets remains a challenge due to dense star interference and the random nature of target motion. To quickly and accurately extract dim space targets from complex star backgrounds, this paper proposes a dim space target detection and track association method for dense star backgrounds. This paper analyzes various features of the target and background from a combined spatiotemporal perspective, including three main stages. First, inter-frame registration is used to filter out bright stars, followed by connected domain post-processing, which simplifies the star map background while enhancing the signal-to-noise ratio of dim targets. Secondly, an image difference fusion coarse processing module is proposed. The reconstructed multi-impulse function is derived from the spectral phase difference to estimate the displacement parameters of different components, after which the image difference fusion is designed to obtain candidate targets. Third, a directional track association algorithm is designed, with the candidate targets as the center and the motion parameters as thresholds, narrowing the association range to a fan-shaped region. This enables fast detection of target tracks while removing excess false alarms. The experimental results on four datasets demonstrate that this method outperforms traditional baseline methods in terms of target detection and localization accuracy.