一种基于贝叶斯网络的微波雷达和图像融合与分类算法
A Microwave Radar and Image Fusion and Classification Algorithm Based on Bayesian Networks

近年来，深度学习技术的进步在合成孔径雷达(SAR)自动目标识别(ATR)技术中取得了出色的表现。然而，由于斑点噪声的干扰，SAR图像的分类任务仍然具有挑战性。为了解决这个问题，本研究提出了一种多尺度局部–全局特征融合网络(MFN)，该网络集成了卷积神经网络(CNN)和Transformer网络。所提出的网络包括三个分支：CovNeXt-SimAM分支，Swin Transformer分支和多尺度特征融合分支。CovNeXt-SimAM分支在不同的尺度上提取SAR图像的局部纹理细节特征。通过将SimAM注意机制结合到CNN块中，从空间和通道注意角度增强了模型的特征提取能力。此外，Swin Transformer分支用于提取不同尺度下的SAR图像全局语义信息。最后，多尺度特征融合分支用于融合局部特征和全局语义信息。此外，为了解决由于经验确定的模型超参数问题而导致模型精度和效率较低的问题，采用贝叶斯超参数优化算法确定了最佳的模型超参数。该研究提出的模型在MSTAR数据集上，标准工作条件(SOCs)和扩展工作条件(EOCs)下，对SAR车辆目标分别取得了99.26%和94.27%的平均识别准确率。与基准模型相比，识别准确率分别提高了12.74%和25.26%。结果表明，贝叶斯-MFN降低了SAR图像之间的类间距离，导致更紧凑的分类特征和更少的斑点噪声干扰。与其他主流模型相比，贝叶斯-MFN模型展现出最佳的分类性能。
In recent years, the advancement of deep learning technology has led to excellent performance in synthetic aperture radar (SAR) automatic target recognition (ATR) technology. However, due to the interference of speckle noise, the task of classifying SAR images remains challenging. To address this issue, a multi-scale local-global feature fusion network (MFN) integrating a convolution neural network (CNN) and a transformer network was proposed in this study. The proposed network comprises three branches: a CovNeXt-SimAM branch, a Swin Transformer branch, and a multi-scale feature fusion branch. The CovNeXt-SimAM branch extracts local texture detail features of the SAR images at different scales. By incorporating the SimAM attention mechanism to the CNN block, the feature extraction capability of the model was enhanced from the perspective of spatial and channel attention. Additionally, the Swin Transformer branch was employed to extract SAR image global semantic information at different scales. Finally, the multi-scale feature fusion branch was used to fuse local features and global semantic information. Moreover, to overcome the problem of poor accuracy and inefficiency of the model due to empirically determined model hyperparameters, the Bayesian hyperparameter optimization algorithm was used to determine the optimal model hyperparameters. The model proposed in this study achieved average recognition accuracies of 99.26% and 94.27% for SAR vehicle targets under standard operating conditions (SOCs) and extended operating conditions (EOCs), respectively, on the MSTAR dataset. Compared with the base-line model, the recognition accuracy has been improved by 12.74% and 25.26%, respectively. The results demonstrated that Bayes-MFN reduces the inter-class distance of the SAR images, resulting in more compact classification features and less interference from speckle noise. Compared with other mainstream models, the Bayes-MFN model exhibited the best