Abstract: Synthetic Aperture Radar (SAR) aircraft target detection and recognition play an irreplaceable strategic role in critical applications such as battlefield situation awareness and national air defense early warning. However, due to the inherent SAR imaging mechanism, the images suffer from discrete scattering points, strong coherent speckle noise, complex backgrounds, and large variations in target scales, making it difficult for existing methods to achieve satisfactory detection accuracy and robustness. To address these challenges, this paper proposes a scattering-prior-driven SAR aircraft detection method with a high-resolution detection head. First, a scattering information enhancement module is designed, where CFAR detection and Gabor filter banks are respectively employed to extract scattering density maps and directional energy maps, thereby improving the model’s perception of strong scattering points and structural directivity of targets. Second, considering the resolution variations in SAR images as well as the size differences among different aircraft categories, we extend the feature pyramid and path aggregation network to the 4× downsampling level based on the YOLOv11m backbone. By adding a P2 high-resolution detection head, the detection performance for multi-scale targets is enhanced. Finally, experimental results on the SAR-Aircraft-1.0 dataset demonstrate that the proposed method achieves 89.3% mAP@0.5 with only 20.7M parameters and 90.6 GFLOPs, validating its advantages in detection accuracy and robustness.