Deployment of corner reflector arrays based on a hybrid attention convolutional neural network

This study proposes an optimization method for the deployment of corner reflector arrays based on a hybrid attention convolutional neural network (CNN), aiming to address the deployment challenges of such arrays on the sea surface. The objective is to enhance the electromagnetic interference capability of corner reflectors against complex targets and to improve the electromagnetic protection effectiveness of maritime combat platforms.

First, the electromagnetic scattering characteristics of corner reflector arrays under various geometric arrangements were analyzed using the shooting and bouncing ray (SBR) method, generating one-dimensional range profiles. Next, the CLEAN algorithm is employed to extract scattering centers from these profiles, producing a dataset containing scattering center positions and radar incidence angles. A predictive model for corner reflector array deployment is then constructed by integrating a CNN with a hybrid attention mechanism. The dataset is fed into the model for training, enabling intelligent prediction of corner reflector deployment. Finally, the model's predictive performance is tested using various ship range profiles, and the predicted corner reflector arrays are compared with actual range profiles to assess prediction accuracy.

The results show that the network achieves a training loss of 0.00027 and improves accuracy by 5.52% compared to the original CNN model, demonstrating its superior prediction accuracy. In scenarios with simple ship scattering characteristics, the one-dimensional range profile of the corner reflector array generated by this method shows a high correlation with that of the ship, with a Pearson correlation coefficient of 0.9137. Even in certain complex scenarios, this method can still formulate effective interference strategies by exploiting the effects of coupled scattering centers.

The research demonstrates that the corner reflector array deployment method based on a hybrid attention CNN effectively optimizes the arrangement of corner reflectors, enhances the ability to interfere with radar detection of targets, and provides a novel technical approach for the electromagnetic protection of maritime combat platforms.