With the proliferation of unmanned aerial vehicles (UAVs) and the escalating electromagnetic environment in space, there has been growing attention and research focus on the strong electromagnetic effects and electromagnetic protection design of UAVs. This paper aims to introduce the potential strong electromagnetic interference that UAVs may encounter during flight. It specifically concentrates on three crucial subsystems: the datalink system, the flight control and navigation system, and the power system. The goal is to provide an overview of the current state of research on the strong electromagnetic effects of UAVs, along with a discussion of commonly employed research methods. Additionally, this paper introduces various means of strong electromagnetic protection that can be utilized for UAVs. Finally, it concludes with a summary of the research status and development trends concerning the effects of strong electromagnetic interference and the corresponding protection measures for UAVs.

Fast nonsingular terminal sliding mode controllers (FNTSMCs) are proposed for underactuated spacecraft formation reconfiguration without either the radial or the in-track thrust. A nonlinear dynamic model of underactuated spacecraft formation is developed, which is then linearized about circular reference orbits. Based on the linearized model, the system controllability is analyzed for either underactuated case. Due to the loss of control in a certain direction, the disturbances consisting of the linearization errors and external perturbations do not enter the system from the same channels as those of the control inputs, and therefore turn into unmatched disturbances. To achieve underactuated formation reconfiguration in the presence of unmatched disturbances, underactuated FNTSMCs are designed to indirectly render the system states convergent to the neighborhood of equilibrium by using the inherent coupling of these system states. Modified FNTSMCs are also presented to enhance the system performa...

A novel planar printed log-periodic dipole array (LPDA) antenna fed by tapered microstrip line (MSL) to double sided parallel strip line (DSPSL) is proposed in this paper. The proposed antenna adopts MSL feeding approach from backside. Using this feeding technique makes the printed LPDA antenna easier to be integrated into radio frequency (RF) circuits. In this paper, four layers are used to construct the antenna. The four layers of the printed LPDA antenna are printed on three thin dielectric substrates which are integrated together. To validate this approach, a printed LPDA antenna is simulated and fabricated for operating in the S and C bands (2.5–6 GHz). The antenna showed a good result over the whole frequency range with 2 : 1 VSWR, an average gain of 6.5 dB, and stable radiation patterns. The measured results are in very good agreement with simulations.

One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a neural network(NN)-based weighted voting classification algorithm is proposed on the basis of the NN-based classifier and combined with the idea of voting strategy, which is implemented on the nodes of the WSN monitoring system by means of the “upper training, lower transplantation” approach. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of about 85% when utilizing a deep neural network (DNN) and deep belief network (DBN) as the base classifier. The experiment reveals that the NN-based weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single NN-based classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater overhead time to run.