Microwave Imaging of Uniaxial Objects Using a Hybrid Input U-Net

This paper introduces hybrid inputs using Internet of Things (IoT) sensors for reconstructing microwave images of uniaxial objects. Specifically, scattered field data is obtained through IoT sensors, and artificial intelligence techniques are employed to enable real-time electromagnetic imaging. The presented method combines a U-Net architecture with an integrated input to reconstruct high-resolution images of dielectric targets for both Transverse Magnetic (TM) and Transverse Electric (TE) waves. The z-axial dielectric constants are reconstructed by the TM wave illumination, while the x- and y-axial dielectric constants are recovered by the TE wave illumination. First, a Direct Sampling Method (DSM) gives spatial details of the target. Second, a Back-propagation (BP) scheme provides basic information about the target’s properties. Lastly, we combine these two inputs by taking their product, which is further processed in the U-Net. Numerical results show that this integration can improve image quality with nearly no additional computing burden. Experiments also reveal that our proposed method is both accurate and efficient for uniaxial objects, making it a reliable solution to overcome the challenges in electromagnetic imaging.