GPU-Accelerated Correction of Atmospheric Errors for Wide-Area Surveillance of Low Earth Orbit Objects

This is the authors' abstract. We don't add key points for this paper.

In this study, we proposed a GPU-based parallel processing algorithm that accelerated atmospheric and ionospheric error correction for the real-time processing of multiple targets detected by a space surveillance radar. The atmosphere was modeled using upper-atmosphere meteorological and ionospheric electron density data, and the propagation characteristics of electromagnetic waves under these conditions were analyzed. Furthermore, we proposed an algorithm to estimate the target positions by considering atmospheric effects when radar echo signals are provided. In particular, when multiple targets were present, the proposed GPU-based parallel processing algorithm enabled the simultaneous correction of errors for all the targets, significantly accelerating the computation speed. The simulation results showed that the proposed GPU-based parallel processing method achieved a speedup of more than one order of magnitude compared with conventional CPU-based sequential processing.