We present a portable 3-D millimeter wave imaging system operating in the K-band (24 GHz). This imaging system consists of a multiple input, multiple output (MIMO) array of 32 transmit elements and 32 receive elements that illuminates a scene with millimeter wave energy and processes the reflected signals to reconstruct a 3-D image. To achieve an acceptable image resolution from this sparse array, the system combines multiple measurements while the sensor is moved relative to the scene being imaged. For ease of portability, the imaging system uses a single Ethernet cable to power the sensor and transfer the captured raw data to a laptop computer. A graphics processing unit (GPU)-optimized image reconstruction algorithm transforms the raw data to a 3-D image with approximately 1 cm voxel resolution, which is rendered in 3-D in a Web browser based user interface. We present measured test images and demonstrate an achieved dimensioning accuracy of ±1 − 3 mm when the system is used to detect and dimension objects hidden behind opaque building materials such as drywall, plywood, ceramic tile, vinyl flooring, and cement.
KEYWORDS: Video, Millimeter wave imaging, 3D image processing, Motion estimation, Imaging systems, Image quality, Video compression, Sensors, Prototyping, K band
Lens-less millimeter-wave (mmWave) imaging of moving objects using a sparse array relies on knowledge of the relative positions between the moving object and the imaging system to enable coherent image reconstruction. However, accurate object position information is rarely available in commercial applications where the moving object, e.g. a conveyor belt or a robot, is controlled independently of the imaging system, or where the imaged objects move autonomously. This poses a significant hurdle for many commercial mmWave imaging applications. We present a video-based motion extraction approach for active mmWave imaging. The object velocity is extracted in real time from motion vectors obtained from a compressed video. This information is combined with readouts from a distance sensor to infer the position of the object at each time instant. Leveraging video-derived motion vectors enables the offloading of computational complexity of 2-D spatial correlations to highly optimized algorithms operating on camera frames. We show experimentally that the image quality of a commercial high-throughput 3-D mmWave imaging system prototype is improved significantly by this approach when the velocity of the target is unknown and time-varying. We furthermore show that image quality is also improved compared to known average motion profiles of the imaged objects. Using a lab setup with known ground truth, we show that the RMS position error is 2.5 mm over a travel length of 0.52 m. This is better than 1/8 of the wavelength at K-band (24 GHz) along the trajectory and thus sufficient to achieve excellent image quality at K-band and longer wavelengths.
Computational imaging is a proven strategy for obtaining high-quality images with fast acquisition rates and simpler hardware. Metasurfaces provide exquisite control over electromagnetic fields, enabling the radiated field to be molded into unique patterns. The fusion of these two concepts can bring about revolutionary advances in the design of imaging systems for security screening. In the context of computational imaging, each field pattern serves as a single measurement of a scene; imaging a scene can then be interpreted as estimating the reflectivity distribution of a target from a set of measurements. As with any computational imaging system, the key challenge is to arrive at a minimal set of measurements from which a diffraction-limited image can be resolved. Here, we show that the information content of a frequency-diverse metasurface aperture can be maximized by design, and used to construct a complete millimeter-wave imaging system spanning a 2 m by 2 m area, consisting of 96 metasurfaces, capable of producing diffraction-limited images of human-scale targets. The metasurfacebased frequency-diverse system presented in this work represents an inexpensive, but tremendously flexible alternative to traditional hardware paradigms, offering the possibility of low-cost, real-time, and ubiquitous screening platforms.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.