## 3 2D MMW Imaging Experiments Using the FMCW Radar Principle and Results  
In this section, we describe the 2D imaging experiments conducted to evaluate the system's ability to capture detailed shape and depth information using the FMCW method. Initially, 2D imaging was performed on three distinct metal shapes: 'F', 'O' (a square shape with a central hollow square), and 'H' as shown in Figure 4. Each object provided unique structural features to test the system's resolution and range detection capabilities.  
The imaging experiments utilized three distinct metal shapes to evaluate the system's sensitivity to external boundaries and internal structures. Figure 5 provides their dimensions. Each object was designed within the GDD imaging system's resolution limits.  
Figure 4: Metal object shapes used for imaging experiments.  
Initially, the FMCW setup enabled the acquisition of an 8 \(\times\) 8 grayscale image of each shape by capturing voltage levels from the reflected radiation path, which was processed by the GDD detection circuit. Through these experiments, range information was derived by mapping signal variations, which were aligned with the optical path difference (OPD) set in the system. Given the system's configuration, achieving simultaneous reference and imaging signals across all points in the 8 \(\times\) 8 grid required strategic mirror positioning for optimal alignment. Due to the limitations in the reference signal's positioning, each object's image was captured over multiple scanning positions, with pixel data combined and aligned based on their respective grid locations. The merged pixel data then provided a final 8 \(\times\) 8 MMW image for each object using FMCW-radar-based imaging, as shown in Figure 6. The asymmetrical \"F\" shape in Figure 6a is particularly affected by the inherent flipping effect of the optical system, which inverts the image both vertically and horizontally. This effect is less noticeable in symmetrical shapes, like the \"O\" and \"H\", making the flipping more prominent in the \"F\" shape.  
These images are generated by processing the electrical signals corresponding to the reflected MMW radiation from the object through heterodyne detection. In these grayscale images, the intensity of the pixels does not directly relate to range data but instead provides a representation of the object's shape based on the variations in the voltage levels. The  
Figure 5: Dimensions of metal objects constructed to fall within the resolution limits of the GDD imaging system. (**a**) “F” shape, (**b**) “O” shape, (**c**) “H” shape.  
Figure 6: MMW images generated using FMCW radar technology for each object, obtained by merging pixel data from multiple scanning positions. (**a**) “F” shape, (**b**) “O” shape, (**c**) “H” shape.  
darker and lighter regions in the image correspond to different intensity levels of the reflected signals, helping to visualize the object's profile. To enhance the signal-to-noise ratio (SNR) and reduce noise in the FMCW signal processing, we employed averaging of multiple raw data samples and implemented beat frequency range selection to focus on the relevant signal components. These techniques effectively suppress noise, ensuring more accurate and reliable range data extraction for improved imaging results.