A 2-D Autofocus Algorithm for Long Synthetic Aperture Time SAR in Low-Contrast Scenarios Based on Deep Neural Network

As synthetic aperture radar (SAR) technology continues to evolve with a focus on miniaturization, reduced weight, and lower costs, its range of applications has broadened to encompass lightweight and slow-moving platforms, such as small autonomous aerial vehicles (AAVs) and airships. However, this advancement introduces a new challenge for SAR systems in the form of excessively long synthetic aperture time (LSAT). LSAT-SAR faces significant challenges caused by its longer integration time (10–1000 times more than conventional airborne SAR’s synthetic aperture time), including more stringent error tolerance of navigation system and more demanding trajectory control requirements. These issues often result in severe 2-D defocusing. Existing high-precision navigation systems and traditional autofocus algorithm, though adequate for conventional airborne SAR, often fail to meet the stringent requirements of LSAT-SAR, especially in low-contrast scenarios, necessitating more efficient and more robust compensation methods. To address these challenges, we propose a 2-D autofocus algorithm for LSAT-SAR using deep neural networks (DNNs). The proposed approach treats the 2-D focusing problem as a series of coupled 1-D curve estimation tasks, employing a weighted entropy loss function. The solution is optimized in an unsupervised manner using a DNN. Finally, the additional refinement is performed through a specialized fine-tuning correction module. Experiments on real LSAT-SAR images with synthetic aperture times of 150–270 s show that the proposed method substantially exceeds the performance of traditional approaches in low-contrast scenarios. It demonstrates excellent robustness, offering valuable insights and technical guidance for LSAT-SAR autofocusing.