Deep-Learning-Based Multiband Signal Fusion for 3-D SAR Superresolution

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 the 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 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. Using two mmWave radars, each with a bandwidth of 4 GHz operating at 60 and 77 GHz, the proposed kR-Net is employed to achieve an effective bandwidth of 21 GHz by robustly estimating the full-band signal. Additionally, the generalizability of the proposed technique is demonstrated across multiband sensing scenarios in the mmWave and terahertz frequencies. Extensive numerical simulations and empirical experiments are conducted to illustrate the superiority of our approach over existing methods for a diverse set of realistic 3-D SAR imaging scenarios.