Millimeter-Wave Imaging Using MIMO-SAR Techniques
There is a strong desire to exploit the non-ionizing millimeter-wave (mmWave) spectrum (from 30 GHz to 300 GHz) in many high-resolution imaging applications ranging from medical to security. The primary challenge of a cost-effective and low-complexity mmWave imaging system is to achieve high-resolution with as few antenna elements as possible. Multiple input multiple-output (MIMO) radars using the simultaneous operation of spatially diverse transmit and receive antennas are good candidates to meet this challenge. On the other hand, higher integration complexity of extremely dense transceiver electronics limits the use of MIMO-only solutions within a relatively large imaging aperture. Hybrid concepts combining synthetic aperture radar (SAR) techniques and MIMO arrays present a great compromise to achieve short data acquisition time and low-complexity. Despite numerous studies that apply MIMO concepts to SAR techniques, the design process of a MIMO-SAR system is non-trivial, especially for mmWave imaging. Many issues have to be carefully addressed. Besides, compared with conventional monostatic sampling schemes, where the measurements are taken by collocated transmit and receive antennas, or MIMO-only solutions, efficient image reconstruction methods for MIMO-SAR topologies are more complicated in short-range applications. This dissertation introduces a complete mmWave imaging solution combining wideband MIMO arrays with SAR techniques, along with computationally efficient novel image reconstruction algorithms optimized for MIMO-SAR configurations. We present highly-integrated and reconfigurable MIMO-SAR testbeds utilizing commercially available complementary metal-oxide semiconductor (CMOS) based system-on-chip wideband MIMO mmWave sensors and motorized rail platforms. Several aspects of the MIMO-SAR testbed design process, including MIMO array calibration, electrical/mechanical synchronization, system-level verification, and performance evaluation, are described. The proposed algorithms are verified by both simulation and processing real data collected with custom-built imaging testbeds. The results confirm that our complete solution presents a strong potential in high-resolution imaging tasks of various real-world applications.