2D Millimeter-Wave Imaging Technology

2D millimeter-wave imaging technology is a non-contact detection method that utilizes millimeter-wave frequency bands for object detection and imaging. Operating between microwave and infrared frequencies, millimeter waves offer excellent penetration capabilities and high resolution, making them highly promising for applications in security screening, medical imaging, and industrial inspection.

The core of this technology involves sensors transmitting millimeter-wave signals and receiving reflected waves, with signal processing algorithms reconstructing two-dimensional images of target objects. Common algorithms include Synthetic Aperture Radar (SAR) techniques and inverse scattering algorithms, which effectively analyze object shapes and material characteristics. From an implementation perspective, SAR algorithms typically involve phase compensation and coherent integration across multiple antenna positions, while inverse scattering methods require solving electromagnetic wave equations through iterative optimization approaches.

For data acquisition, users need to download test data packages from specified addresses. These datasets typically contain raw radar echo signals or preprocessed imaging data. Practical verification confirms that these datasets support testing and optimization of imaging algorithms, providing reliable foundations for research and applications. In code implementation, developers often use Python or MATLAB to process these datasets, employing functions like signal filtering, Fast Fourier Transform (FFT) for range compression, and backprojection algorithms for image formation.

The advantages of 2D millimeter-wave imaging lie in its non-ionizing nature and penetration capability through non-metallic materials, making it suitable for security checks and non-destructive testing scenarios. As hardware and algorithms advance, this technology is expected to achieve greater breakthroughs in automation and high-precision imaging fields. Future algorithmic improvements may incorporate machine learning techniques for enhanced image classification and adaptive signal processing.