Multistatic microwave imaging with arrays of planar cavities

Abstract

The authors present a multistatic imaging system at microwave frequencies based on arrays of planar cavity sub‐apertures, or panels. The cavity imager consists of sets of transmit and receive panels, loaded with radiating irises distributed over the sub‐apertures in an aperiodic pattern. This frequency‐diverse aperture produces distinct radiation patterns as a function of frequency that encode scene information onto a set of measurements; images are subsequently reconstructed using computational imaging approaches. Similar to previously reported computational imaging systems, the cavity‐based imager presents a simple system architecture, minimising the number and expense of components required in traditional microwave imaging systems. The cavity imager builds on previous frequency‐diverse approaches, such as the recently reported metamaterial and air‐filled cavity systems, by utilising frequency‐diverse panels for both the transmit and receive sub‐apertures of the imaging system. Though the panel‐to‐panel architecture has greater sensitivity to calibration error, this implementation nevertheless increases mode diversity and, in the context of a computational imaging system, results in improved image reconstructions.

Computational microwave imaging using 3D printed conductive polymer frequency‐diverse metasurface antennas

“…Increasing the Q-factor is desirable in that it reduces the spatial overlap between radiation patterns, reducing the redundancy of the information collected from the scene as the frequency is swept. However, the Q-factor of a frequency-diverse antenna is inversely proportional to the radiation efficiency, which governs the signal-to-noise ratio (SNR) for imaging [21,23]. As a result, there is a trade-off between the Q-factor and the radiation efficiency of a frequency-diverse antenna, which needs to be tailored for the requirements of the desired application.…”

“…Recently, the concept of frequency-diversity leveraging computational imaging has been shown to be a promising alternative to address these challenges [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Computational imaging techniques enable the system hardware architecture to be simplified by moving the burden from the hardware layer to the image processing (software) layer [30][31][32][33][34].…”

Large Metasurface Aperture for Millimeter Wave Computational Imaging at the Human-Scale

“…Subwavelength irises, etched into the top copper surface, sample the waveguide mode and transmit or receive radiation. The spatially varying waveguide modes within the irregular circuit board cavity feed the irises, which in turn produce distinct radiation patterns that vary as a function of the driving frequency 27 28 29 30 .…”

Frequency-Diverse Computational Automotive Radar Technique for Debris Detection

“…With a Q-factor 330, the time domain impulse response of the frequency diverse antenna is on the order of 20 ns [3], significantly smaller than the assumed dwell time, td=99 µs. A detailed discussion on the design of frequency-diverse computational antennas is given in [35]. For a vehicle traveling at a speed of 30 mph, the maximum Doppler frequency can be calculated to be 3.6 kHz at 80 GHz, resulting in a coherence time of 9.28 ms, which is substantially larger than the dwell time td=99 µs at each frequency sampling point.…”

“…The frequency-diverse antenna is modelled as an array of cavity-backed metamaterial elements radiating across the 77-81 GHz operating frequency band. The radiation mechanism for each metamaterial element can be derived in the form of a magnetic dipole and the antenna radiated fields propagated to the imaged scene are then calculated using dyadic Green's functions at each frequency to form the sensing matrix H [35]. For this PSF analysis, we study three cases.…”