For K-distributed sea clutter, a constant false alarm rate (CFAR) is crucial as a desired property for automatic target detection in an unknown and non-stationary background. In multiple-target scenarios, the target masking effect reduces the detection performance of CFAR detectors evidently. A machine learning based processor, associating the artificial neural network (ANN) and a clustering algorithm of density-based spatial clustering of applications with noise (DBSCAN), namely, DBSCAN-CFAR, is proposed herein to address this issue. ANN is trained with a symmetrical structure to estimate the shape parameter of background clutter, whereas DBSCAN is devoted to excluding interference targets and sea spikes as outliers in the leading and lagging windows that are symmetrical about the cell under test (CUT). Simulation results verified that the ANN-based method provides the optimal parameter estimation results in the range of 0.1 to 30, which facilitates the control of actual false alarm probability. The effectiveness and robustness of DBSCAN-CFAR are also confirmed by the comparisons of conventional CFAR processors in different clutter conditions, comprised of varying target numbers, shape parameters, and false alarm probabilities. Although the proposed ANN-based DBSCAN-CFAR processor incurs more elapsed time, it achieves superior CFAR performance without a prior knowledge on the number and distribution of interference targets.

The emergence and popularization of various fifth-generation fighter jets with supersonic cruise, super maneuverability, and stealth functionalities have raised higher and more comprehensive challenges for the tactical performance and operational indicators of air defense weapon systems. The training of air defense systems requires simulated targets; however, the traditional targets cannot simulate the radar cross-section (RCS) distribution characteristics of fifth-generation fighter aircrafts. In addition, the existing target aircrafts are expensive and cannot be mass-produced. Therefore, in this paper, a corner reflector and a Luneburg ball reflector with RCS distribution characteristics of a fifth-generation fighter in a certain spatial area are designed for target simulation. Several corner reflectors and Luneburg balls are used to form an array to realize the simulations. The RCS value and distribution characteristics of the target can be combined with fuzzy clustering and a single-chip microcomputer to design an intelligent switching system, which improves the practicability of the intelligent target design proposed in this paper.

A fast analytical method has been proposed for predicting the shielding effectiveness (SE) and resonances of an apertured enclosure with an interior enclosure. Under the concept of electromagnetic topology, the monitor point and the walls are treated as nodes, and the space between them is treated as tubes. The propagation relationships at tube level and reflection relationships at node level are derived as the propagation matrix. After modeling the front wall of the interior enclosure as a junction between two waveguides, an equivalent circuital model of the enclosures is derived. The front wall and the window structure in front of the adjacent space of the interior enclosure are considered as a three-port scattering matrix. Then we can use the extended BLT equations to calculate the voltage response at each node. Results from the proposed method are compared with those from the numerical method, and the results have a good agreement while it can dramatically save calculation time.