Aiming at the problems of detail blurring and structural distortion caused by speckle noise, additive white noise and hybrid noise interference in synthetic aperture radar (SAR) images, this paper proposes a Dynamic Adaptive Transformer Network (DAT-Net) integrating a dynamic gated attention module and a frequency-domain multi-expert enhancement module for SAR image denoising. The proposed model leverages a multi-scale encoder–decoder framework, combining local convolutional feature extraction with global self-attention mechanisms to transcend the limitations of conventional approaches restricted to single noise types, thereby achieving adaptive suppression of multi-source noise contamination. Key innovations comprise the following: (1) A Dynamic Gated Attention Module (DGAM) employing dual-path feature embedding and dynamic thresholding mechanisms to precisely characterize noise spatial heterogeneity; (2) A Frequency-domain Multi-Expert Enhancement (FMEE) Module utilizing Fourier decomposition and expert network ensembles for collaborative optimization of high-frequency and low-frequency components; (3) Lightweight Multi-scale Convolution Blocks (MCB) enhancing cross-scale feature fusion capabilities. Experimental results demonstrate that DAT-Net achieves quantifiable performance enhancement in both simulated and real SAR environments. Compared with other denoising algorithms, the proposed methodology exhibits superior noise suppression across diverse noise scenarios while preserving intrinsic textural features.

In order to simulate the nonlinear interference effect of high-power electromagnetic signals on airborne Synthetic Aperture Radar (SAR) imaging, the walk–stop geometric modeling of airborne SAR was established, and the range time-domain pulse coherence method was used to simulate SAR echo. By establishing a SAR receiver RF channel model, the interference mechanism of high-power electromagnetic signals is combined on RF front-end devices, the nonlinear effects on the echo signal when high-power electromagnetic signals enter the receiver are simulated, and finally the range Doppler algorithm is used to image the echo signal and the interference signal. A method for evaluating the interference effect of high-power electromagnetic signals on airborne SAR imaging using the correlation coefficient of images was proposed. The results showed that the change in the correlation coefficient of images before and after interference relative to the interference signal ratio was consistent with the image interference effect. The correlation coefficient showed a stepped trend with the change in the interference signal ratio, and there was a saturation effect after the interference effect reached a certain level.

Airborne synthetic aperture radar (SAR) serves as critical battlefield reconnaissance equipment, yet it remains vulnerable to electromagnetic interference (EMI) in combat environments, leading to image-quality degradation. To address this challenge, this study proposes an EMI-effect prediction framework for airborne SAR electromagnetic environments, based on the Newton–Raphson-based optimization (NRBO) and XGBoost algorithms. The methodology enables interference-level prediction through electromagnetic signal parameters obtained from reconnaissance operations, providing operational foundations with which SAR systems can mitigate the impacts of EMI. A laboratory-based airborne SAR EMI test system was developed to establish mapping relationships between EMI signal parameters and SAR imaging performance degradation. This experimental platform facilitated EMI-effect investigations across diverse interference scenarios. An evaluation methodology for SAR image degradation caused by EMI was formulated, revealing the characteristic influence patterns of different interference signals in the context of SAR imagery. The NRBO–XGBoost framework was established through algorithmic integration of Newton–Raphson search principles with trap avoidance mechanisms from the Newton–Raphson optimization algorithm, optimizing the XGBoost hyperparameters. Utilizing the developed test system, comprehensive EMI datasets were constructed under varied interference conditions. Comparative experiments demonstrated the NRBO–XGBoost model’s superior accuracy and generalization performance relative to conventional prediction approaches.

To enhance the survivability of unmanned aerial vehicles (UAVs) in complex electromagnetic environments, a model is presented to assess the complex electromagnetic interference (EMI) effects on UAV data links. Based on the mechanism of electromagnetic interference, three key parameters are introduced: the loss-of-lock threshold A_t, the effect–time ratio D, and the effect index τ. An assessment model is then developed using these parameters. By classifying interference into sinusoidal-type and noise-type, the model is capable of predicting the interference effects of complex interference scenarios comprising in-band single-tone, partial-band noise, and out-of-band interferences that generate in-band third-order intermodulation components. Measurements of A_t and D from single-source EMI effect tests, along with validation from three-source and four-source EMI effect tests, confirm the model’s efficacy. Results indicate that the A_t is inversely proportional to the D and correlates with the bit error rate. The maximum error between the experimental and theoretical values of τ is 0.709 dB, demonstrating the validity and applicability of the model. Finally, a four-level EMI effect assessment method was proposed. The assessment method could provide theoretical support for anti-interference decision-making systems and enhance the UAVs’ anti-interference capability in complex electromagnetic environments.