Performance Boundaries and Tradeoffs in Super-Resolution Imaging Technologies for Space Targets

Inverse synthetic aperture radar (ISAR) super-resolution imaging technology is widely applied in space target imaging. However, the performance limits of super-resolution imaging algorithms remain largely unexplored. Our work addresses this gap by deriving mathematical expressions for the upper and lower bounds of cross-range resolution in ISAR imaging based on the computational resolution limit (CRL) theory for line spectrum reconstruction. Leveraging these explicit expressions, we first explore influencing factors of these bounds, including the traditional Rayleigh limit, number of scatterers, and peak signal-to-noise ratio (PSNR) of the scatterers. Then, we elucidate the minimum resource requirements in ISAR imaging imposed by CRL theory to meet the desired cross-range resolution, without which studying super-resolution algorithms becomes unnecessary in practice. Furthermore, we analyze the tradeoffs between the cumulative rotation angle, radar transmit energy, and other factors that contribute to optimizing the resolution. Simulations are conducted to demonstrate these tradeoffs across various ISAR imaging scenarios, revealing their high dependence on specific imaging targets.