This letter presents a novel broadband microstrip patch antenna with a simple geometry. Two parasitic mushroom-type arrays are incorporated along with the two radiating edges of a main radiating patch. Firstly, thanks to the mushroom-type structure, a new resonant mode, characterized as quasi-TM30 mode, is generated. Besides, the main radiating patch produces the original TM10mode. Thus, wideband performance is realized based on the two combined modes. Secondly, the current distributions on those metal patches are nearly uniform so that high gains are achieved over the entire operating bandwidth. Measured results indicate that the enhanced impedance bandwidth is from 11.9 to 18.2 GHz, which covers the whole Ku-band. Meanwhile, a nearly constant peak-radiating gain between 10 and 10.5 dBi at broadside radiation is obtained. The proposed antenna maintains the advantages of wide bandwidth, ease of fabrication, flat and high gains, and a low profile less than $\lambda_{0}/13$ thickness substrate.

Substrate integrated waveguide (SIW) is a planar waveguide structure, which offers the advantages of low profile, ease of fabrication, low insertion loss, and compatibility with other planar circuits. SIW end-fire antennas have drawn broad interests due to the potential applications in aircraft, missile, and radar systems. However, this planar structure suffers from narrow bandwidth due to severe impedance mismatch at the radiating aperture. Meanwhile, the narrow radiating aperture of SIW end-fire antennas also deteriorates the radiation performance. This paper presents a detailed review upon the most recent research efforts concerning the improvement of antenna performances. They are discussed and classified into three different categories from the aspect of polarization properties: horizontally polarized (HP), vertically polarized (VP) and circularly polarized (CP) SIW end-fire antennas. Some practical difficulties for the development of SIW end-fire antennas are pointed out and effective approaches are also provided. A wide variety of antenna examples are presented with respect to theoretical and experimental results.

Feature selection is an important issue in the field of machine learning, which can reduce misleading computations and improve classification performance. Generally, feature selection can be considered as a binary optimization problem. Gravitational Search Algorithm (GSA) is a population-based heuristic algorithm inspired by Newton’s laws of gravity and motion. Although GSA shows good performance in solving optimization problems, it has a shortcoming of premature convergence. In this paper, the concept of global memory is introduced and the definition of exponential $Kbest$ is used in an improved version of GSA called IGSA. In this algorithm, the position of the optimal solution obtained so far is memorized, which can effectively prevent particles from gathering together and moving slowly. In this way, the exploitation ability of the algorithm gets improved, and a proper balance between exploration and exploitation gets established. Besides, the exponential $Kbest$ can significantly decrease the running time. In order to solve feature selection problem, a binary IGSA (BIGSA) is further introduced. The proposed algorithm is tested on a set of standard datasets and compared with other algorithms. The experimental results confirm the high efficiency of BIGSA for feature selection.

The communication relationship can reflect the behavior relationship between different communication targets. The in-depth analysis of the communication relationship can obtain the behaviors of communication individuals, and speculate their hierarchical positions in the communication network, so as to provide a basis for further speculation on the structure of the communication network. For massive spectrum signals, we can also obtain important information such as communication relationships and behaviors of communication individuals, without cracking the signal content, but by analyzing the physical characteristics and statistical laws of the spectrum signals. In order to overcome the difficulties and costs of analyzing communication behaviors from cracking the signal content in existing research, this paper studies the physical characteristics and statistical laws of spectrum signals based on the features of frequency hopping period, average power and time of signal occurrence. Because the spectrum signals generated by the communication individuals show clustering characteristics, this paper proposes a communication relationship mining method based on improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The method can accurately discover the communication relationship of the radio station from the incomplete spectrum monitoring data, without cracking the content carried by the spectrum signals, which provides a new idea for the mining and analysis of mass spectrum monitoring signals.

Radio frequency (RF)-based drone identification is a safety-critical task, where erroneous model outputs may result in potential costs. However, most existing studies are based on the closed-set assumption, with only a few studies considering the presence of out-of-distribution (OOD) samples during testing. This paper argues that a reliable model should strive to minimize errors in classification, necessitating the ability to detect both OOD and misclassified ID samples (IDꭗ), as both types can lead to model misclassifications. We term this task Reliable Drone RF Signal Identification (RDI). To address the RDI task, this paper proposes an extreme value theory (EVT)-based method for simultaneously detecting these two types of samples. Initially, we propose employing a Generalized Pareto Distribution (GPD) model and establishing an uncertainty scoring function for each ID class, based on correctly identified in-distribution samples (ID). The proposed function effectively assigns lower uncertainty to ID and higher uncertainty to IDꭗ and OOD samples. Moreover, we tackle the GPD threshold selection issue, given its paramount significance in the construction of the GPD model. This paper proposes a GPD threshold selection algorithm based on minimized Kullback-Leibler (KL) divergence (MKL-based GPDTS). MKL-based GPDTS computes an appropriate GPD threshold for each ID class, based on the information of that class. We conduct comprehensive validation of the proposed approach using a self-collected drone RF signal dataset and an open-source dataset. The experimental results demonstrate the effectiveness of the proposed method.

Regulation of drones is already an important research topic. The drone identification method based on the radio frequency (RF) signal analysis technology is an efficient approach to regulate mainstream civilian drones. However, existing research on drone identification has been conducted under high signal-to-noise ratio (SNR) conditions. In fact, long-range drone identification in urban environments is performed under low SNR conditions. In this article, a global context (GC)-based threshold strategy is proposed to solve the above problem. The threshold is automatically determined during the deep architecture optimization. Meanwhile, the unimportant features in the network are removed by threshold denoising. First, the weight of each feature channel is obtained by modeling cross-channel dependencies, and then a weighted average is performed for each position along the feature channel on the feature map to obtain the thresholds. The method reduces the impact of unimportant features on performance without complex data preprocessing. In the experiments, the performance and complexity of the proposed method are extensively evaluated by 11 different drone RF signals. The experimental results show that in comparison with other deep learning-based threshold calculation methods, the proposed method has a more outstanding performance and lower computational complexity for drone RF signal identification under the low SNR condition.