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.

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For precise detection and positioning of weapons and equipment under complex ground backgrounds and weather-changing aerial backgrounds. Compared with the traditional convolutional neural networks, the Capsule Network (CapsNet) is more suitable for identifying weapons and equipment in complex backgrounds because it uses vectors as input for the first time, which can well retain the characteristic information such as the direction and the angle of the target. Therefore, this paper proposes a radar target classification algorithm based on the combination of CapsNetv2 and infrared lidar, which simplifies the convolutional layer of the traditional 9 × 9 capsule network through a 1 × 1 reduction layer and a 3 × 3 convolution kernel, and adopts a double-layer capsule layer. Two prediction frames are obtained to improve the recognition accuracy; at the same time, the output volume retains the direction and the angle, which can more accurately classify the radar targets in various complex backgrounds. Applying the method proposed in this article to the MSTAR dataset shows that the radar target positioning is accurate. The rate increases to 99.5%. Finally, compared with the AlexNet and the YOLOv4 methods designed by Alex Krizhevsky, the proposed radar target recognition method can accurately and quickly identify weapons and equipment from complex backgrounds. The results obtained from the CapsNetv2 are accurately compared with other methods’ in complex backgrounds. The proposed method significantly improves the efficiency of military inspections.