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 99 capsule network through a 11 reduction layer and a 33 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.

Traditional range–instantaneous Doppler (RID) methods for maneuvering target imaging are hindered by issues related to low resolution and inadequate noise suppression. To address this, we propose a novel ISAR imaging method enhanced by deep learning, which incorporates the fundamental architecture of CapsNet along with two additional convolutional layers. Pre-training is conducted through the deep learning network to establish the mapping function for reference. Subsequently, the trained network is integrated into the electromagnetic simulation software, Feko 2019, utilizing a combination of geometric forms such as corner reflectors and Luneberg spheres for analysis. The results indicate that the derived ISAR imaging effectively identifies the ISAR program associated with complex aerial targets. A thorough analysis of the imaging results further corroborates the effectiveness and superiority of this approach. Both simulation and empirical data demonstrate that this method significantly enhances imaging resolution and noise suppression.

Gait recognition is a remote biometric technology that utilizes the dynamic characteristics of human movement to identify individuals even under various extreme lighting conditions. Due to the limitation in spatial perception capability inherent in 2D gait representations, LiDAR can directly capture 3D gait features and represent them as point clouds, reducing environmental and lighting interference in recognition while significantly advancing privacy protection. For complex 3D representations, shallow networks fail to achieve accurate recognition, making vision Transformers the foremost prevalent method. However, the prevalence of dumb patches has limited the widespread use of Transformer architecture in gait recognition. This paper proposes a method named HorGait, which utilizes a hybrid model with a Transformer architecture for gait recognition on the planar projection of 3D point clouds from LiDAR. Specifically, it employs a hybrid model structure called LHM Block to achieve input adaptation, long-range, and high-order spatial interaction of the Transformer architecture. Additionally, it uses large convolutional kernel CNNs to segment the input representation, replacing attention windows to reduce dumb patches. Extensive experiments demonstrated that HorGait achieved a Rank-1 accuracy of 82.54% on the SUSTech1K dataset, surpassing the state-of-the-art Transformer architecture method by 8.33%. This confirms the hybrid model’s ability to execute the complete Transformer process and excel in point cloud planar projection. The outstanding performance of HorGait offers new insights for the future application of the Transformer architecture in gait recognition.

Electromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in balancing the accuracy and generalization ability of the model. For example, the Radar Cross Section (RCS) distribution characteristics of a single corner reflector model or Luneberg lens provide a relatively stable RCS value within a certain airspace range, which to some extent reduces the difficulty of radar target detection and fails to truly evaluate the radar performance. This paper aims to propose an innovative multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning. By selecting common targets such as reflectors and Luneberg lens reflectors as optimization variables, a deep neural network model is constructed and trained with a large amount of electromagnetic data to achieve high-precision fitting of the target electromagnetic characteristics. Meanwhile, an advanced genetic optimization algorithm is introduced to optimize the multiple parameters of the model to meet the error index requirements of radar target detection. In this paper, by combining specific optimization variables such as corner reflectors and Luneberg lenses with the deep learning model and genetic algorithm, the deficiencies of traditional methods in handling electromagnetic characteristic fitting are effectively addressed. The experimental results show that the 60° corner reflector successfully realizes the simulation of multiple peak characteristics of the target, and the Luneberg lens reflector achieves the simulation of a relatively small RCS average value with certain fluctuations in a large space range, which strongly proves that this method has significant advantages in improving the fitting accuracy and optimization efficiency, opening up new avenues for research and application in the electromagnetic field.