Doctor of Philosophy, The Ohio State University, 2021, Electrical and Computer Engineering

Passive radar systems use signals of opportunity to illuminate targets instead of dedicated radar transmitters. The signals of opportunity have lower bandwidth than dedicated active radar systems, leading to poor downrange resolution. Multiple signals of opportunity can be coherently combined to increase the overall bandwidth of the system, and therefore create finer resolution images. These signals are usually separated in the frequency domain (non-contiguous), which causes large unwanted grating lobe artifacts in the image when using back-projection or Fourier transform based imaging. Additionally, the signals of opportunity may be completely uncorrelated and transmitting from different locations. This dissertation investigates methods of combining these signals to create images with higher resolution than if only a single signal of opportunity were used. A method to quickly estimate bistatic scattered electric fields from complex targets is augmented with new models. The targets are first decomposed into a set of canonical geometries with closed-form solutions. Then the total scattered field of the target is found as the superposition of the scattered fields from the individual geometries. The canonical geometries used are plates, dihedrals, and trihedrals. A closed-form solution for the non-90° dihedral is introduced and verified with iterative physical optics. Bistatic SAR images of complex targets can be predicted in seconds using the total scattered fields from the canonical geometries, whereas it would take hours using a physical optics solver. Approaches of combining signals for 1D passive radar imaging are then examined. The signals may be non-contiguous in frequency, and originate from transmitters not located at the same position. A calibration method is developed to align the downrange responses, and coherently combine the two signals. A compressive sensing-based algorithm is used to combine the non-contiguous frequency data, and is shown to mitigat (open full item for complete abstract)

Committee: Robert Burkholder (Advisor); Brian Joseph (Committee Member); Fernando Teixeira (Committee Member); Joel Johnson (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2017, Electrical and Computer Engineering

Multi-channel wideband radar has proven to be an indispensable tool for many surveillance applications. However, achieving higher resolution with current architectures comes at the cost of lower dynamic range for the sensor. Recent theoretical advances in the area of compressive sensing provide a new framework for sampling and processing sensor signals at a rate that scales with the information content and complexity of the scene. For the case of delay estimation - a core problem in radar sensing - compressive sensing provides a theoretical guarantee for successful recovery using K\log (N/K) compressed measurements of K scatterers over a delay space of N bins. Previous practical implementations of compressive sampling radar attempted to reduce sampling complexity at the expense of increased complexity in receivers realizing unstructured random projections. In this thesis, we study the problem of developing structured acquisition systems that exploit the underlying structure of radar signals to provide provable performance guarantees and reduced design complexity . Broadly, our work is divided into two parts. In the first part, we present a compressive radar design that employs structured waveforms on transmit and reduced complexity sub-sampling on receive with recovery guarantees of target parameters at sub-Nyquist rates. The proposed framework lends itself to practical hardware implementation as it utilizes standard linear frequency modulated waveforms mixed with sinusoidal tones and receivers with an approximated matched filter termed as stretch processor and a uniform sampling rate Analog to digital converter (ADC). Also, this structure simplifies the calibration step in practical systems because the number of random elements is minimized. We extend this illumination approach to a multiple input and output (MIMO) radar architecture and establish uniform as well as non-uniform recovery guarantees, given a sufficient number of modulating tones. We also prese (open full item for complete abstract)

Committee: Emre Ertin (Advisor); Lee Potter (Committee Member); Yuejie Chi (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2022, Electrical and Computer Engineering

This dissertation addresses two separate engineering challenges: image-inverse problems and novelty detection. First, we address image-inverse problems. We review Plug-and-Play (PnP) algorithms, where a proximal operator is replaced by a call of an arbitrary denoising algorithm. We apply PnP algorithms to compressive Magnetic Resonance Imaging (MRI). MRI is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. However, when compared to other clinical imaging modalities (e.g., CT or ultrasound), the data acquisition process for MRI is inherently slow, which motivates undersampling and thus drives the need for ac- curate, efficient reconstruction methods from undersampled datasets. We apply the PnP-ADMM algorithm to cardiac MRI and knee MRI data. For these algorithms, we developed learned denoisers that can process complex-valued MRI images. Our algorithms achieve state-of-the-art performance on both the cardiac and knee datasets. Regularization by Denoising (RED), as proposed by Romano, Elad, and Milanfar, is a powerful image-recovery framework that aims to minimize an explicit regular- ization objective constructed from a plug-in image-denoising function. Experimental evidence suggests that RED algorithms are state-of-the-art. We claim, however, that explicit regularization does not explain the RED algorithms. In particular, we show that many of the expressions in the paper by Romano et al. hold only when the denoiser has a symmetric Jacobian, and we demonstrate that such symmetry does not occur with practical denoisers such as non-local means, BM3D, TNRD, and DnCNN. To explain the RED algorithms, we propose a new framework called Score-Matching by Denoising (SMD), which aims to match a “score” (i.e., the gradient of a log-prior). Novelty detection is the ability for a machine learning system to detect signals that are significantly different from samples seen during training. Detecting novelties is (open full item for complete abstract)

Committee: Philip Schniter (Advisor); Rizwan Ahmad (Committee Member); Lee Potter (Committee Member) Subjects: Electrical Engineering

Master of Science in Computer Engineering (MSCE), Wright State University, 2021, Computer Engineering

We consider the problem of compressive synthetic aperture radar (SAR) imaging with the goal of reconstructing SAR imagery in the presence of undersampled phase history. While this problem is typically considered in compressive sensing (CS) literature, we consider a variety of deep learning approaches where a deep neural network (DNN) is trained to form SAR imagery from limited data. At the cost of computationally intensive offline training, on-line test-time DNN-SAR has demonstrated orders of magnitude faster reconstruction than standard CS algorithms. A limitation of the DNN approach is that any change to the operating conditions necessitates a costly retraining procedure. In this work, we consider development of DNN methods that are robust to discrepancies between training and testing conditions. We examine several approaches to this problem, including using input-layer dropout, augmented data support indicators, and DNN-based robust approximate message passing.

Committee: Joshua Ash Ph.D. (Advisor); Tanvi Banerjee Ph.D. (Committee Member); Mateen Rizki Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science; Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2019, Electrical and Computer Engineering

We present novel realizations of computational terahertz (THz) imaging techniques based on compressive sensing and phase-retrieval algorithms and a single-pixel THz sensor. Imaging in the THz band covering 300 GHz-10 THz is being considered for key applications in biomedical imaging, security screening and non-destructive evaluation. State-of-the-art in THz imaging is based on mechanical raster scanning using a single, high-performance sensor. Such raster-scanning imagers are rather bulky and suffer from very low frame-rates, as well as mechanical noise due to the moving parts in the hardware. Alternatively, multi-detector imagers such as THz focal plane arrays (FPAs) can speed-up image acquisition time, potentially reaching real-time video rates. However, such devices require complex and expensive fabrication and they typically exhibit limited sensitivity due to additional noise introduced by the read-out circuit. In this dissertation, we demonstrate novel THz imaging techniques based on a single THz sensor that concurrently circumvent the slow acquisition time and mechanical noise of raster scan imaging. This is achieved by using an optically reconfigurable spatial wave modulation scheme to "serialize'' the scene measurements. Subsequently, compressive sensing (CS) and reconstruction algorithms are employed to computationally generate 2D images of the scene from a set of serial measurements, each corresponding to a different spatial modulation. Similar to well-developed optical CS methods, compressive THz imaging allows far fewer measurements than the conventional Nyquist rate to accurately reconstruct sparse scenes. In addition, compressive THz reconstruction exhibits better signal-to-noise (SNR) performance compared to the FPA cameras. To enable the study and experimental demonstration of various computational imaging algorithms, we realize a generalized compressive THz imaging setup using conventional quasi-optical components and a semiconductor-based ph (open full item for complete abstract)

Committee: Kubilay Sertel (Advisor); Niru K. Nahar (Committee Member); Fernando Teixeira (Committee Member); Robert Burkholder (Committee Member) Subjects: Electrical Engineering; Electromagnetics; Optics

MS, University of Cincinnati, 2012, Engineering and Applied Science: Electrical Engineering

Compressed Sensing reconstructs the signal / image from a significantly less number of samples violating the Nyquist criteria. It exploits the sparsity present in the signal /image. Medical Imaging techniques like MRI (Magnetic Resonance Imaging), MRA (Magnetic Resonance Angiography), PET (Positron Emission Tomography) and MRSI (Magnetic Resonance Spectroscopic Imaging) are very popular and powerful medical tools and are used throughout the globe. The drawback associated with these important tools is that they have very slow data acquisition processes. On the other hand, all natural images are sparse in nature in some transform domain. Magnetic Resonance Angiograms are sparse in the image domain itself. More complex images like Magnetic Resonance Imaging of brain is sparse in some transform domain like Wavelet Transform etc. Compressed Sensing using this property of the medical images could significantly change the concept of scanning associated with the devices used in the sense that Compressed Sensing when applied could speed up the scanning process by a large margin. Using the inherent sparsity in the medical images, Compressed Sensing undersamples the k-space by acquiring very small amount of data from it and reconstructs the original image using non-linear optimization method. In this thesis, we have worked on the sampling schemes or patterns used to undersample the k-space or the Fourier space of different medical imaging techniques. Our sampling scheme when compared to the ones proposed by Dr. Lustig[1, 2] in his work, gives a better output. For a proper comparison, same amount of the data was acquired and the results were compared.

Committee: H. Howard Fan PhD (Committee Chair); T. Douglas Mast PhD (Committee Member); William Wee PhD (Committee Member) Subjects: Electrical Engineering