The complex electromagnetic environment makes the electromagnetic susceptibility (EMS) test challenging for the unmanned aerial vehicle (UAV) data link. The EMS test can be regarded as an optimization problem to obtain the maximum signal-to-interference ratio that makes the data link losing lock. In this article, we proposed an adaptive EMS test design method based on Bayesian optimization to expand the ability of the EMS test toward the UAV data link. The sensitive electromagnetic interference parameters can be automatically searched according to the adaptive test design method. The Gaussian process is used as the surrogate model for the EMS threshold. Besides, acquisition functions are discussed, including the probability of improvement, expected improvement, and upper confidence bound to balance the exploration and exploitation. Then, two test examples are applied to verify the adaptive test design method. Compared with the traditional EMS test methods, the proposed method introducing Bayesian optimization into the EMS test design can significantly improve the test efficiency and achieve an excellent human-in-the-loop effect.

To assess the survivability of an unmanned aerial vehicle (UAV) in a complex electromagnetic environment, a novel method for assessing electromagnetic interference (EMI) threats to a UAV is introduced. A dataset of loss-of-lock thresholds for the UAV data link was generated through EMI injection tests. A genetic algorithm (GA)-optimized extreme gradient boosting (XGBoost) was then applied to efficiently predict these loss-of-lock thresholds. And Shapley additive explanations (SHAP) were used to measure the importance of features. Compared with K-nearest neighbor, support vector machine, decision tree, and XGBoost, GA-XGBoost shows better prediction accuracy and overall performance. Based on this, a GA-XGBoost-based assessment method was proposed to classify EMI effects into four levels using a three-level effect index. Finally, the EMI effect levels and SHAP results were used to formulate targeted anti-interference strategies. The proposed method can help to improve the anti-interference performance of UAVs.

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.

The application scenarios of uncrewed aerial vehicles (UAVs) are becoming increasingly diverse. Enhancing their safety and stability in complex electromagnetic environments remains a challenge. This article investigates the electromagnetic effects and protection of mainstream consumer-grade multirotor UAVs subjected to ultrawideband electromagnetic pulse (UWB EMP) irradiation. The coupling voltages on the UAV's GPS antenna, datalink antenna, and typical cables under UWB EMP exposure are obtained through simulation. The effects of different radiation parameters on UAVs are evaluated via UWB EMP irradiation experiments. Results indicate that UWB EMP primarily enters the UAV through back-door coupling, inducing high voltages on the internal cables—particularly those between the electronic speed controller and the rotor motors. The high voltages and overheating will lead to mosfet breakdown and burnout of the motor's copper windings. Additionally, UWB EMP irradiation can cause abnormalities in the inertial measurement unit, resulting in UAV crashes. In contrast, front-door coupling effects mainly manifest as temporary interference like image distortion and datalink interruption. Finally, electromagnetic protection measures are applied to the back-door pathways of the UAV, and their effectiveness is validated.

In this paper, a receiving scheme for intermediate frequency (IF) signals enhanced by stochastic resonance (SR) is proposed. The proposed scheme mitigates the reception failure of these signals, which can occur in radio and communication systems under extremely low signal-to-noise ratio (SNR). The SR mechanism for enhancing sinusoidal signals is analyzed. An analytic solution with time parameters of the Fokker-Planck Equation (FPE) is obtained by introducing the decision time from the non-autonomous FPE into an autonomous one. A quadratic polynomial receiving structure for sinusoidal signals enhanced by SR is proposed by comparing the characteristics of energy detection and matched filter detection. And the polynomial coefficients of the quadratic system are obtained by maximizing the deflection. Based on the idea of “the average of N samples” and the assumption of Gaussian distribution approximation under the law of large numbers, a quadratic polynomial receiving scheme for sinusoidal signals enhanced by SR is proposed. The conclusions are as below: 1) when the noise intensity is constant, the smaller the correlation time, the bigger the local SNR around the IF frequency due to the better performance of the low-pass filter; 2) The error bit ratio of the quadratic polynomial receiver is less than 2 × 10-2 when N =20 and the SNR is above -14 dB, which can be applied to the military emergency communication under extremely low SNR. Experiment verifies the theory.

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.