## 6 Future Work  
The current setup showcases the potential of FMCW radar for 3D imaging; however, several avenues for enhancement and optimization remain to be explored. A primary focus of future work is the automation of the dual-axis positioning system for mirror adjustments. Currently, mirror positions are manually fine-tuned to achieve optimal focus, which is time-consuming and susceptible to human error. To address these limitations, we aim to integrate a motorized dual-axis positioning mirror system featuring precise vertical (pitch) and horizontal (yaw) controls. Leveraging a Dual-Axis Antenna Positioner with azimuth and elevation control, this system will provide a 360\({}^{\circ}\) continuous travel range, utilizing a heavy-duty azimuth rotation stage driven by a stepper or servo motor. Automating these adjustments will significantly enhance the measurement precision, improve the operational efficiency, and reduce user intervention, resulting in a more robust and accurate imaging setup. To achieve proper range detection, careful adjustments in the curvature and positioning of the reference signal mirror are required, ensuring that the beam spot is well focused and evenly distributed across the image plane, thereby enhancing the consistency and accuracy of the acquired images. Another key area of development is transitioning from the current raster scanning methodology, which relies on a single GDD, to an FPA-based imaging system. While raster scanning has proven effective, it can be relatively slow, particularly for complex or large objects, limiting the speed and efficiency of image acquisition. An FPA imaging system, combined with a dedicated quasi-optical design, will enable simultaneous data acquisition across multiple detection points. This will drastically enhance the imaging speed, allowing for high-resolution, real-time 3D reconstructions. The FPA system will also improve data quality and provide finer details, making the setup more suitable for advanced applications. In the transition from raster scanning to an FPA-based imaging system, several challenges and benefits need to be addressed. Key challenges include the non-uniformityof individual GDDs, which may result in inconsistent detection performance across the array, and the difficulty of ensuring a uniform reference signal distribution across all pixels in the FPA. The implementation of an FPA system provides significant benefits, notably enhanced imaging speed, as it captures multiple pixels simultaneously, thereby reducing the image acquisition time. Additionally, FPA systems allow for sub-pixel imaging, which can enhance resolution and provide a more detailed, accurate representation of the scanned area, improving the overall quality of the imaging system. The FPA-based system will require the development of smaller GDDs with uniform response characteristics and a redesigned optical setup to ensure consistent illumination across the array. The FPA system is expected to significantly enhance imaging speed by eliminating the need for mechanical raster scanning, enabling real-time data acquisition, while also improving data quality through a higher spatial resolution and an enhanced signal-to-noise ratio. Also, we aim to explore the application of few-shot learning and metric learning techniques, as proposed in [17], to enhance the classification and recognition of complex objects in MMW imaging. By integrating such AI-based approaches, we believe the system can improve its ability to identify intricate structures in MMW images with minimal labeled data. These improvements will be instrumental in expanding the technology's applicability in high-performance FMCW-radar-based imaging systems. Conceptualization, A.R.K. and D.R. ; methodology, A.R.K. and D.R. ; software, A.R.K. ; validation, A.A., N.K. and Y.Y. ; formal analysis, D.R. ; investigation, A.R.K. ; resources, A.R.K. and D.R. ; data curation, A.R.K. ; writing--original draft preparation, A.R.K. ; writing--review and editing, D.R., A.A., N.K. and Y.Y. ; visualization, A.R.K.