Andreas Pedross-Engel

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

We demonstrate a low-profile holographic imaging system at millimeter wavelengths based on an aperture composed of frequency-diverse metasurfaces. Utilizing measurements of spatially-diverse field patterns, diffraction-limited images of human-sized subjects are reconstructed. The system is driven by a single microwave source swept over a band of frequencies (17.5–26.5 GHz) and switched between a collection of transmit and receive metasurface panels. High fidelity image reconstruction requires a precise model for each field pattern generated by the aperture, as well as the manner in which the field scatters from objects in the scene. This constraint makes scaling of computational imaging systems inherently challenging for electrically large, coherent apertures. To meet the demanding requirements, we introduce computational methods and calibration approaches that enable rapid and accurate imaging performance.

Synthetic aperture radar with dynamic metasurface antennas: a conceptual development

We investigate the application of dynamic metasurface antennas (DMAs) to synthetic aperture radar (SAR) systems. Metasurface antennas can generate a multitude of tailored electromagnetic waveforms from a physical platform that is low-cost, lightweight, and planar; these characteristics are not readily available with traditional SAR technologies, such as phased arrays and mechanically steered systems. We show that electronically tuned DMAs can generate steerable, directive beams for traditional stripmap and spotlight SAR imaging modes. This capability eliminates the need for mechanical gimbals and phase shifters, simplifying the hardware architecture of a SAR system. Additionally, we discuss alternative imaging modalities, including enhanced resolution stripmap and diverse pattern stripmap, which can achieve resolution on par with spotlight, while maintaining a large region-of-interest, as possible with stripmap. Further consideration is given to strategies for integrating metasurfaces with chirped pulse RF sources. DMAs are poised to propel SAR systems forward by offering a vast range of capabilities from a significantly improved physical platform.

Partitioned inverse image reconstruction for millimeter-wave SAR imaging

Synthetic aperture radar (SAR) images are representations of the microwave or millimeter-wave reflectivity of the observed scenes. SAR image reconstruction is an inverse problem, which can be solved via an approximation, e.g. matched filter (MF), or the explicit inverse using a large amount of measurement data. However, the approximation limits the resolution while the explicit inverse is computationally complex and mostly ill-conditioned. This paper proposes a partitioned inverse (PI) approach based on the Moore-Penrose pseudo inverse using truncated singular value decomposition for regularization, which is robust to noise. It is shown that PI has an improved resolution of 24% over MF even at 0 dB SNR and is three orders of magnitude faster than the explicit inverse. A measurement based proof of concept experiment using a laboratory K-Band (15–26.5 GHz) ultra-wideband SAR system is shown to validate the proposed approach.

Enhanced Resolution Stripmap Mode Using Dynamic Metasurface Antennas

To maintain sufficient signal-to-noise ratio (SNR) for image reconstruction and image interpretation, conventional synthetic aperture radar (SAR) systems must trade off resolution and scene size. This paper proposes a new SAR mode of operation, which improves resolution while maintaining good SNR and a large scene size. It leverages the unique properties of dynamic metasurface antennas (MSAs) to subsample a large virtual beamwidth utilizing multiple small distinct antenna beams. Due to this parallelization in scene sampling, the constraints on the azimuth sampling rate can be relaxed while maintaining an aliasing-free cross range. Due to the versatile properties of MSAs and their cost effective manufacturing process, this paper proposes SAR systems, which can obtain high resolution images over a wide scene size with lower cost and complexity than competing approaches. Point-spread functions and proof-of-concept SAR simulations are shown to verify this approach. In addition, laboratory experiments using a commercial prototype MSA are presented, which show an improvement of 62% in cross-range resolution of the proposed approach, compared with the cross-range resolution of stripmap mode SAR with the same aperture.

A low cost 10.0–11.1 GHz X-band microwave backscatter communication testbed with integrated planar wideband antennas

We present a low cost X-band microwave backscatter communication testbed incorporating integrated planar wideband bow-tie antennas. This testbed leverages low cost silicon germanium (SiGe) integrated circuits intended for the satellite television market to provide an experimental capability for backscatter experimentation in the 10.0 GHz to 11.1 GHz band. The integrated wideband bow-tie antennas have a 10 dB return loss bandwidth of 9.63-13.15 GHz and a gain of 6 dBi. The transmitter consists of a single-chip frequency synthesizer and buffer amplifier with an output power of -2 dBm. The receive downconverter consists of an integrated LNA, mixer, and LO frequency synthesizer, with an RF frequency range of 10.0 GHz to 13.0 GHz and an IF frequency range of 250 MHz to 3250 MHz. The downconverters measured conversion gain is 36 dB with a noise figure of 7 dB. The IF signal processing leverages a Universal Software Radio Peripheral (USRP) to support arbitrary backscatter modulation schemes such as ASK, PSK, or QAM backscatter with an IF bandwidth up to 50 MHz. We found good agreement between the measured and simulated free-space path loss at ranges of up to 4.1 m in a laboratory environment, and demonstrate the use of this testbed in a BPSK backscatter configuration at a rate of 10 Mbps.

GPU accelerated partitioned reconstruction algorithm for millimeter-wave 3D synthetic aperture radar (SAR) images

3D reconstruction using synthetic aperture radar (SAR) imaging is a computationally complex process due to the large amount of data involved. This paper proposes a partitioned reconstruction method for 3D SAR imaging, which leads to computationally efficient algorithms. The proposed method allows for parallel processing, e.g. using a general purpose graphic processing unit (GPU). Experimental results using a laboratory K-Band (15–26.5 GHz) ultra-wideband SAR system are presented. It is shown that 3D SAR reconstruction with GPU acceleration using the proposed algorithms is 300 times faster than the conventional matched filter approach.

2D and 3D millimeter-wave synthetic aperture radar imaging on a PR2 platform

Optical depth cameras, including both time-of-flight and structured-light sensors, have led to dramatic improvements in robot sensing and perception. We propose the use of millimeter-wave (mmW) radar as an important complement to optical sensors. While the millimeter wavelengths of radar sensors do not support as high resolution as the nanometer wavelength of optical sensors, the ability of mmW signals to penetrate smoky and foggy environments as well as see through many opaque objects makes them a compelling sensor for navigation as well as manipulation in challenging environments. We present a series of 2D and 3D mmW images made with a hand-held antenna grasped by a PR2 robot. The radar image sensor uses a mechanical “painting” motion to acquire multiple views of the target object over the 15 - 26.5 GHz K-band. A GPU-based reconstruction algorithm synthesizes 2D and 3D images of the target object. We demonstrate a ground range resolution of 13.6 mm and a cross-range resolution of 7.1 mm for objects up to 0.5 m away from the robot. We further demonstrate imaging objects through fog, as well as through opaque paper.

Simultaneous Imaging, Sensor Tag Localization, and Backscatter Uplink via Synthetic Aperture Radar

This paper presents an extension of synthetic aperture radar (SAR) techniques to enable simultaneous radar imaging, sensor tag localization, and backscatter-based data uplink from multiple sensor tags in a cluttered environment. A unified system model is presented that leverages coherent processing of backscattered signals gathered over the synthetic aperture for all three of these purposes. The proposed approach, using balanced orthogonal codes for SAR-based localization as well as the backscatter data uplink, is shown to have several favorable properties, including straightforward tag-vs-clutter discrimination, straightforward multiple access among tags, and improved signal-to-noise ratio during localization. A proof-of-principle indoor experiment is presented in the X-band (10–13 GHz) using two custom-designed backscatter tags interrogated by a vector network analyzer functioning as an FMCW radar. The proposed system model is validated by simultaneous imaging of a cluttered scene, tag localization with a maximum range error of 9 mm, and data demodulation from both tags telemetering temperature changes at a rate of 1 bit/s at ranges of 4.4 m and 4.7 m. The resulting point-spread functions of tags demonstrate a range resolution of 4.7 cm and a cross-range resolution of 9.1 cm.

Modeling and Identification of Ultra-Wideband Analog Multipliers

Analog multipliers are employed in many applications. In conventional RF front ends, for example, they are widely used for frequency conversion tasks. In noncoherent energy detectors or autocorrelation receivers, they multiply the (broadband) input signal by itself to achieve a down-conversion. Unfortunately, there exist no ideal hardware realizations of such devices, hence multipliers inevitably create undesired signal content at their output. To be able to deal with these effects or correct for them, we need to be able to model and identify realistic RF multipliers. This paper proposes and validates a multiple-input single-output Wiener–Hammerstein model for ultra-wideband (UWB) analog multipliers. The structure of the proposed model gives insight in the distortions created. It thus provides the possibility to study the realistic behavior of systems involving those multipliers, e.g., the influence of undesired nonlinear signal content onto noncoherent UWB receivers. A comparison of the model performance is shown with respect to measurements and circuit simulations.

Security screening via computational imaging using frequency-diverse metasurface apertures

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.