## 3 Microwave NDT Techniques
### Microwave Imaging Method  
Microwave imaging (MWI) involves emitting microwave signals toward a test object from different directions and analyzing the reflected signals to create an image of the material's surface or interior. This method helps detect flaws or inhomogeneities inside materials, as well as characterizing material properties. Figure 9 depicts a block diagram of a MWI system. In the system, a specimen is surrounded by several antennas within the imaging domain. Each antenna acts as a transmitter and receiver successively. When an antenna is in transmit mode, it radiates the specimen with an electromagnetic signal; the reflected scattered signals from the specimen are measured by the other antennas, which are in receive mode. After collecting all the measurements, they are processed using an inversion algorithm to obtain information about the electrical properties of the specimen. For example, as shown in Figure 9, the information is represented as a color map of the dielectric constant. Researchers have developed several methods and techniques to detect material defects using microwave imaging. For example, a study conducted by [PERSON] and [PERSON] [88] proposed a system that features an innovative integrated sensor unit that combines a microwave antenna with two laser displacement sensors. This unit is designed to automatically trace the contour of the tested material while maintaining a consistent stand-off distance. As it moves, the system measures the reflected signals to construct images of the material. This robust system provides instant insights into any defects in the structure. The authors of [89] presented another example of a microwave imaging system. The researchers designed an X-band (8-12 GHz) microwave imaging system to collect data for examining different multilayered structures. In this system, ultra-wideband (UWB) noise waveforms were utilized. Furthermore, the system could accurately locate internal boundaries, inclusions, or defects in tested samples [89]. The findings indicate that meticulous selection of the UWB noise waveforms can significantly help in accurately determining the  
Figure 9: A block diagram of a microwave imaging system. Each antenna acts as a transmitter and receiver. As a transmitter, the antenna radiates the specimen, and the scattered electric fields are measured by receivers surrounding it. The collected data are processed using an inversion algorithm to obtain a color map of the electrical properties of the specimen with defects. size of defects within materials, with the notable advantage of having high immunity from high RF interference; this is due to the random nature of the UWB signals, as opposed to traditional deterministic waveforms [89]. [PERSON] et al. [90] proposed a microwave imaging technique that effectively solves inverse scattering equations by leveraging a priori information about the scenario under examination and employing a cost-efficient numerical method for field prediction. The technique proposed in [90] used the Sherman-Morrison-Woodbury (SMW) updating formula for electric field computations, showcasing great effectiveness in defect retrieval while maintaining a low computational cost. Further exploitation of a priori knowledge of the scenario under test can be utilized to extend the proposed procedure to three-dimensional geometries [90]. Further, the authors in [91] presented a review of microwave imaging systems to detect breast cancer. In this application, a breast is illuminated by various sources of electromagnetic signals from multiple angles. For each source, the scattered field data are collected. The measured data are processed using algorithms that solve the inverse scattering problem associated with microwave imaging. The algorithm outputs color maps that represent the electrical properties of the breast. [PERSON] et al. [92] introduced a microwave imaging technique for detecting cracks in concrete structures using UWB signals.