Three-dimensional (3-D) synthetic aperture radar (SAR) is widely used in many security and industrial applications requiring high-resolution imaging of concealed or occluded objects. The ability to resolve intricate 3-D targets is essential to the performance of such applications and depends directly on system bandwidth. However, because high-bandwidth systems face several prohibitive hurdles, an alternative solution is to operate multiple radars at distinct frequency bands and fuse the multiband signals. Current multiband signal fusion methods assume a simple target model and a small number of point reflectors, which is invalid for realistic security screening and industrial imaging scenarios wherein the target model effectively consists of a large number of reflectors. To the best of our knowledge, this study presents the first use of deep learning for multiband signal fusion. The proposed network, called kR-Net, employs a hybrid, dual-domain complex-valued convolutional neural network (CV-CNN) to fuse multiband signals and impute the missing samples in the frequency gaps between subbands. By exploiting the relationships in both the wavenumber domain and wavenumber spectral domain, the proposed framework overcomes the drawbacks of existing multiband imaging techniques for realistic scenarios at a fraction of the computation time of existing multiband fusion algorithms. Our method achieves high-resolution imaging of intricate targets previously impossible using conventional techniques and enables finer resolution capacity for concealed weapon detection and occluded object classification using multiband signaling without requiring more advanced hardware. Furthermore, a fully integrated multiband imaging system is developed using commercially available millimeter-wave (mmWave) radars for efficient multiband imaging.

翻译：三维合成孔径雷达（3-D SAR）在需要高分辨率成像隐蔽或遮挡物体的诸多安防与工业应用中广泛使用。解析复杂三维目标的能力直接影响这类应用的性能，并严格依赖于系统带宽。然而，由于高带宽系统面临诸多难以克服的障碍，一种替代方案是使用多个雷达在独立频段工作并融合多频段信号。现有融合方法假设目标模型简单且点反射体数量较少，但这在实际安检与工业成像场景中并不成立，因为此类场景中的目标模型实际上由大量反射体构成。目前据我们所知，本研究首次提出将深度学习用于多频段信号融合。所提出的网络kR-Net采用混合双域复数卷积神经网络（CV-CNN）融合多频段信号，并填补子频段间频率间隙中的缺失采样。通过利用波数域和波数谱域中的相关性，该框架克服了现有多频段成像技术在真实场景中的缺陷，且计算时间仅为现有多频段融合算法的一小部分。我们的方法实现了传统技术无法达到的复杂目标高分辨率成像，无需更先进的硬件即可通过多频段信号实现对隐藏武器检测与遮挡物体分类的更精细分辨能力。此外，我们基于商用毫米波雷达开发了一套完整的多频段成像系统，实现了高效的多频段成像。