Optimized techniques for real-time microwave and millimeter wave SAR imaging

Microwave and millimeter wave synthetic aperture radar (SAR)-based imaging techniques, used for nondestructive evaluation (NDE), have shown tremendous usefulness for the inspection of a wide variety of complex composite materials and structures. Studies were performed for the optimization of uniform and nonuniform sampling (i.e., measurement positions) since existing formulations of SAR resolution and sampling criteria do not account for all of the physical characteristics of a measurement (e.g., 2D limited-size aperture, electric field decreasing with distance from the measuring antenna, etc.) and nonuniform sampling criteria supports sampling below the Nyquist rate. The results of these studies demonstrate optimum sampling given design requirements that fully explain resolution dependence on sampling criteria. This work was then extended to manually-selected and nonuniformly distributed samples such that the intelligence of the user may be utilized by observing SAR images being updated in real-time. Furthermore, a novel reconstruction method was devised that uses components of the SAR algorithm to advantageously exploit the inherent spatial information contained in the data, resulting in a superior final SAR image. Furthermore, better SAR images can be obtained if multiple frequencies are utilized as compared to single frequency. To this end, the design of an existing microwave imaging array was modified to support multiple frequency measurement. Lastly, the data of interest in such an array may be corrupted by coupling among elements since they are closely spaced, resulting in images with an increased level of artifacts. A method for correcting or pre-processing the data by using an adaptation of correlation canceling technique is presented as well --Abstract, page iii

Compressive sensing for 3D microwave imaging systems

Compressed sensing (CS) image reconstruction techniques are developed and experimentally implemented for wideband microwave synthetic aperture radar (SAR) imaging systems with applications to nondestructive testing and evaluation. These techniques significantly reduce the number of spatial measurement points and, consequently, the acquisition time by sampling at a level lower than the Nyquist-Shannon rate. Benefiting from a reduced number of samples, this work successfully implemented two scanning procedures: the nonuniform raster and the optimum path. Three CS reconstruction approaches are also proposed for the wideband microwave SAR-based imaging systems. The first approach reconstructs a full-set of raw data from undersampled measurements via L1-norm optimization and consequently applies 3D forward SAR on the reconstructed raw data. The second proposed approach employs forward SAR and reverse SAR (R-SAR) transforms in each L1-norm optimization iteration reconstructing images directly. This dissertation proposes a simple, elegant truncation repair method to combat the truncation error which is a critical obstacle to the convergence of the CS iterative algorithm. The third proposed CS reconstruction algorithm is the adaptive basis selection (ABS) compressed sensing. Rather than a fixed sparsifying basis, the proposed ABS method adaptively selects the best basis from a set of bases in each iteration of the L1-norm optimization according to a proposed decision metric that is derived from the sparsity of the image and the coherence between the measurement and sparsifying matrices. The results of several experiments indicate that the proposed algorithms recover 2D and 3D SAR images with only 20% of the spatial points and reduce the acquisition time by up to 66% of that of conventional methods while maintaining or improving the quality of the SAR images --Abstract, page iv

Free-Hand Scanning and Imaging

Wideband synthetic aperture radar (SAR) imaging. A probe transmits a signal through its aperture incident to an object located in a medium of interest remotely from the probe. The probe receives through the aperture a plurality of nonuniformly sampled reflected signals from the object as the probe moves in a measurement plane located a predetermined distance from the object. A processor executes a SAR-based reconstruction algorithm to generate an image

Signal processing for microwave imaging systems with very sparse array

This dissertation investigates image reconstruction algorithms for near-field, two dimensional (2D) synthetic aperture radar (SAR) using compressed sensing (CS) based methods. In conventional SAR imaging systems, acquiring higher-quality images requires longer measuring time and/or more elements in an antenna array. Millimeter wave imaging systems using evenly-spaced antenna arrays also have spatial resolution constraints due to the large size of the antennas. This dissertation applies the CS principle to a bistatic antenna array that consists of separate transmitter and receiver subarrays very sparsely and non-uniformly distributed on a 2D plane. One pair of transmitter and receiver elements is turned on at a time, and different pairs are turned on in series to achieve synthetic aperture and controlled random measurements. This dissertation contributes to CS-hardware co-design by proposing several signal-processing methods, including monostatic approximation, re-gridding, adaptive interpolation, CS-based reconstruction, and image denoising. The proposed algorithms enable the successful implementation of CS-SAR hardware cameras, improve the resolution and image quality, and reduce hardware cost and experiment time. This dissertation also describes and analyzes the results for each independent method. The algorithms proposed in this dissertation break the limitations of hardware configuration. By using 16 x 16 transmit and receive elements with an average space of 16 mm, the sparse-array camera achieves the image resolution of 2 mm. This is equivalent to six percent of the λ/4 evenly-spaced array. The reconstructed images achieve similar quality as the fully-sampled array with the structure similarity (SSIM) larger than 0.8 and peak signal-to-noise ratio (PSNR) greater than 25 --Abstract, page iv

Hardware architectures for compact microwave and millimeter wave cameras

Millimeter wave SAR imaging has shown promise as an inspection tool for human skin for characterizing burns and skin cancers. However, the current state-of-the-art in microwave camera technology is not yet suited for developing a millimeter wave camera for human skin inspection. Consequently, the objective of this dissertation has been to build the necessary foundation of research to achieve such a millimeter wave camera. First, frequency uncertainty in signals generated by a practical microwave source, which is prone to drift in output frequency, was studied to determine its effect on SAR-generated images. A direct relationship was found between the level of image distortions caused by frequency uncertainty and the product of frequency uncertainty and distance between the imaging measurement grid and sample under test. The second investigation involved the development of a millimeter wave imaging system that forms the basic building block for a millimeter wave camera. The imaging system, composed of two system-on-chip transmitters and receivers and an antipodal Vivaldi-style antenna, operated in the 58-64 GHz frequency range and employed the ω-k SAR algorithm. Imaging tests on burnt pigskin showed its potential for imaging and characterizing flaws in skin. The final investigation involved the development of a new microwave imaging methodology, named Chaotic Excitation Synthetic Aperture Radar (CESAR), for designing microwave and millimeter wave cameras at a fraction of the size and hardware complexity of previous systems. CESAR is based on transmitting and receiving from all antennas in a planar array simultaneously. A small microwave camera operating in the 23-25 GHz frequency was designed and fabricated based on CESAR. Imaging results with the camera showed it was capable of basic feature detection for various applications --Abstract, page iv

Sparse emission source microscopy for rapid emission source imaging

In Paper I, Sparse Emission Source Microscopy (ESM) methodology will be introduced and discussed for the localization of major EMI radiation sources in complex and large systems. Traditional ESM method takes abundant and uniformly-distributed scanning points on the scanning plane using a robotic system, which can provide high-quality source images but consumes too much time. This section presents a sparse and nonuniform sampling technique for ESM, which is more time-efficient in identifying major radiation sources, even though the image quality is sacrificed. The feasibility of sparse sampling is mathematically proved, and it is shown that increasing number of points increases the signal-to-noise ratio (SNR) of reconstructed images. What\u27s more, a nearest neighbor interpolation method is utilized to estimate the radiated power in real-time scanning. Thus, back-propagated images and estimated radiated power can be obtained in real-time measurement, which can efficiently and instantaneously provide the locations and the radiation strengths of the most significant emission sources. In Paper II, EMI coupling paths and mitigation of optical transceiver modules are investigated. Optical transceiver modules are commonly used in telecommunication and data communication systems, and are significantly troublesome at their operation frequencies and/or harmonics. In this section, simulations and measurements are performed on optical transceiver modules, and total radiated power (TRP) is also measured, to identify and characterize the EMI coupling paths. Currents on the silicon photonic sub-assembly conductor housing and optical fiber connection ferrule are identified as a dominant radiating source. EMI mitigation methods are developed and shown to be effective in reducing the radiated emissions from real product hardware --Abstract, page iv

Sparse nonlinear optimization for signal processing and communications

This dissertation proposes three classes of new sparse nonlinear optimization algorithms for network echo cancellation (NEC), 3-D synthetic aperture radar (SAR) image reconstruction, and adaptive turbo equalization in multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications, respectively. For NEC, the proposed two proportionate affine projection sign algorithms (APSAs) utilize the sparse nature of the network impulse response (NIR). Benefiting from the characteristics of l₁-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the conventional adaptive algorithms. For 3-D SAR image reconstruction, the proposed two compressed sensing (CS) approaches exploit the sparse nature of the SAR holographic image. Combining CS with the range migration algorithms (RMAs), these approaches can decrease the load of data acquisition while recovering satisfactory 3-D SAR image through l₁-norm optimization. For MIMO UWA communications, a robust iterative channel estimation based minimum mean-square-error (MMSE) turbo equalizer is proposed for large MIMO detection. The MIMO channel estimation is performed jointly with the MMSE equalizer and the maximum a posteriori probability (MAP) decoder. The proposed MIMO detection scheme has been tested by experimental data and proved to be robust against tough MIMO channels. --Abstract, page iv

Synthetic aperture radar-based techniques and reconfigurable antenna design for microwave imaging of layered structures

In the past several decades, a number of microwave imaging techniques have been developed for detecting embedded objects (targets) in a homogeneous media. New applications such as nondestructive testing of layered composite structures, through-wall and medical imaging require more advanced imaging systems and image reconstruction algorithms (post-processing) suitable for imaging inhomogeneous (i.e., layered) media. Currently-available imaging algorithms are not always robust, easy to implement, and fast. Synthetic aperture radar (SAR) techniques are some of the more prominent approaches for image reconstruction when considering low loss and homogeneous media. To address limitations of SAR imaging, when interested in imaging an embedded object in an inhomogeneous media with loss, two different methods are introduced, namely; modified piecewise SAR (MPW-SAR) and Wiener filter-based layered SAR (WL-SAR). From imaging system hardware point-of-view, microwave imaging systems require suitable antennas for signal transmission and data collection. A reconfigurable antenna which its characteristics can be dynamically changed provide significant flexibility in terms of beam-forming, reduction in unwanted noise and multiplicity of use including for imaging applications. However, despite these potentially advantageous characteristics, the field of reconfigurable antenna design is fairly new and there is not a methodical design procedure. This issue is addressed by introducing an organized design method for a reconfigurable antenna capable of operating in several distinct frequency bands. The design constraints (e.g., size and gain) can also be included. Based on this method, a novel reconfigurable coplanar waveguide-fed slot antenna is designed to cover several different frequency bands while keeping the antenna size as small as possible --Abstract, page iii

Addendum to proceedings of the 1978 Synthetic Aperture Radar Technology Conference

Various research projects on synthetic aperture radar are reported, including SAR calibration techniques. Slot arrays, sidelobe suppression, and wide swaths on satellite-borne radar were examined. The SAR applied to remote sensing was also considered