Unmanned aerial vehicle (UAV), when illuminated by high-power microwave (HPM), is vulnerable to losing control and even falling to the ground with damage. To explore the mechanism of the UAV's falling and its damage caused by HPM, the HPM radiation experiments on a certain kind of UAV are carried out. It is concluded that the UAV's cable is the main energy coupling path from HPM according to the analysis of the cable connection relationships among the flight control system (FCS), the electronic speed control (ESC) board, and the motors based on the experiment. Furthermore, the coupled voltages of the cable's termination load are obtained when the UAV is radiated by HPM with different carrier frequencies or incident directions using numerical calculation and typical test verification. To facilitate the analysis of circuit level, the equivalence of radiation and injection test is analyzed, so the waveform of the control signal related to the UAV's flight is recorded by the injection test. According to the experimental results and the principle of the UAV's flight dynamics, the effect mechanisms of the UAV's falling and its damage are clarified. The experimental results show that the carrier frequency of the HPM leading to the falling and damage of the UAV mainly depends on the length of the internal cable inside the UAV. The pulsewidth and the repetition frequency of HPM resulting in the UAV's falling are related to the pulsewidth and the period of the control signal of FCS. In addition, the coupled voltages induced on the twisted three-wire cables due to the illumination of HPM are verified to enter into the control signal port through parasitic capacitance and, finally, to damage the vulnerable components, including the metal–oxide–semiconductor field-effect transistor inside the ESC and the motor of the UAV. The damage mechanism is determined to be the high-voltage breakdown and large current burning.

In this article, a novel method is presented for electromagnetic interference (EMI) perception and electromagnetic susceptibility (EMS) assessment of an unmanned aerial vehicle (UAV) against interference. A large number of in-phase and quadrature data of the UAV's data link and their corresponding critical EMI signal were collected by the equivalent injection test method. After visualization and preprocessing of the in-phase and quadrature data, the obtained time–frequency spectrum and the data-link state parameter histogram were joined to further preprocess as the input of the model. A dual-channel convolutional neural network optimized by a sparrow search algorithm (SSA-DCNN) was proposed to predict the EMS of the data link of the UAV. The proposed method can not only avoid manual extraction of data features but also improve the intelligence level of EMI perception and safety evaluation on UAVs. The performance of the dual-channel convolutional neural network is compared with that of the SSA-DCNN method under different optimization algorithms. The results show that the SSA-DCNN model can significantly enhance the convergence speed and prediction accuracy of the training process compared with the dual-channel convolutional neural network. Besides, among the optimization algorithms, the stochastic gradient descent with momentum applied to the SSA-DCNN model shows a better performance.

The datalink of an unmanned aerial vehicle (UAV) is vulnerable to external electromagnetic interference (EMI). The characteristic parameters of the EMI signal can be definitively detected by a developed monitoring platform mounted on the UAV, but it is impossible to determine the lost-link thresholds of the datalink in any state by experiment because the state of the datalink varies dynamically with the flight distance and the UAV's attitudes. In this case, whether a certain state of the UAV's datalink will be interrupted or not by the EMI cannot be evaluated. In this article, a method of EMI prediction for the UAV's dynamic datalink based on the Gaussian process regression (GPR) is proposed, two indicators, including the interfering frequency and the operation signal power of the datalink, are taken as the training inputs, and the lost-link thresholds of the datalink obtained by the EMI injection test are taken as the training targets. The results show that this method can effectively fit the training samples and determine a 95% confidence interval of the predictive outputs, which has a low predictive error, less than 2.2 dB. Compared with the methods of the layer recurrent neural network and support vector machine, the GPR method has higher predictive accuracy and a stronger generalization ability. Therefore, the detected EMI characteristic parameters can be used as new input samples to predict the lost-link thresholds in any condition. On this basis, the EMI margin can be calculated to evaluate the degree of the anti-EMI redundancy of the UAV's datalink, which can be used for the active adaptation of the UAV's datalink to EMI in the future.

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.

The detection of an electromagnetic pulse (EMP) field is of great significance in determining the field environment of tested equipment in small spaces. Finger-shaped miniature optical fiber sensors for electromagnetic pulse field measurement were designed. The antenna of a weak field sensor was integrated with a shielding shell, and the wire welded at the direct electro-optic converting circuit connected to an optical fiber through special structure and circuit design was taken as the antenna of a strong field sensor. Measurements in the time domain and frequency domain had been carried out for the two sensors. Experiment results demonstrate that the weak field sensor and the strong field sensor have flat responses from 100 kHz to 1 GHz with a variation of 2.3 dB and 2.9 dB, respectively, and the EMP waveform detected by the sensors agrees well with the applied standard square wave. Moreover, the strong field sensor exhibits linear responses from 645 V/m to 83 kV/m. The resolution of the weak field sensor is as low as 13 V/m. The result indicated that the designed sensors had good performance.

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.