A synthetic aperture radar (SAR) raw signal simulator is useful for supporting algorithm innovation, system scheme verification, etc. Trajectory deviation is a realistic factor that should be considered in a SAR raw signal simulator and is very important for applications such as motion composition and image formation for a SAR with nonlinear trajectory. However, existing efficient simulators become deteriorated and even invalid when the magnitude of trajectory deviation increases. Therefore, we designed an efficient SAR raw signal simulator that accounts for large trajectory deviation. Based on spatial spectrum analysis of the SAR raw signal, it is disclosed and verified that the 2D spatial frequency spectrum of the SAR raw signal is an arc of a circle at a fixed transmitted signal frequency. Based on this finding, the proposed method calculates the SAR raw signal by curvilinear integral in the 2D frequency domain. Compared with existing methods, it can precisely simulate the SAR raw signal in the case that the deviation radius is much larger. Moreover, taking advantage of the fast Fourier transform (FFT), the computational complexity of this method is much less than the time-domain ones. Furthermore, this method is applicable for multiple SAR acquisition modes and diverse waveforms and compatible with radar antenna beam width, squint angle, radar signal bandwidth, and trajectory fluctuation. Experimental results show its outstanding performance for simulating the raw signal of SAR with large trajectory deviation.

The introduction of noise during image acquisition and transmission is inevitable, leading to a significant reduction in the accuracy of image processing tasks, such as target classification, localization, and recognition. To address this issue, this paper proposes a novel robustness-oriented pooling module called Gaussian adaptive pooling. Drawing on the principles of Gaussian filters, the method introduces a Gaussian weight for feature values in the pooling operation, thus integrating filtering and pooling in a novel manner. This approach is both lightweight and versatile, requiring no additional learnable parameters, and enables seamless integration into neural network architectures with pooling layers. Rigorous mathematical derivations and simulation experiments show that our proposed Gaussian adaptive pooling method surpasses conventional methods (average-pooling and max-pooling) in noise handling. Furthermore, its robustness is comparable to traditional pooling methods in addressing challenges such as rotations, scalings, and translations. Extensive evaluations across multiple computer vision tasks—including image classification (CIFAR-10/100), object detection (MS COCO and RTTS), and semantic segmentation (CamVid)—confirm its effectiveness. Specifically, under varying levels of noise and degraded conditions, Gaussian adaptive pooling achieves significant improvements in standard performance metrics compared to conventional pooling methods. For instance, it delivers notable quantitative gains across different tasks including up to a 12.67% increase in mean intersection over union on the CamVid dataset for semantic segmentation and a 1.1% mAP50 enhancement on the real-world RTTS dataset for object detection.