Master of Science (M.S.), University of Dayton, 2017, Electrical and Computer Engineering

As radar technology development advances and more devices are employed in traditional frequency allocation bands, such as the microwave portion of the frequency spectrum, users are increasingly struggling to operate amidst this spectrum congestion. With spectrum congestion on the rise, application performance degradation is progressively being realized due to scarce available bandwidth. Therefore, users, such as the 5G wireless community and the automotive industry, are exploring applications at higher portions of the frequency spectrum with such efforts being focused in the millimeter wave (MMW) frequency bands. A number of novel applications, such as full-body imaging and automotive collision avoidance systems, have been improved on or realized with the aid of MMW frequencies and their associated phenomenology. However, this portion of the spectrum lags, in some cases by orders of magnitude, far behind in research and development in comparison to other bands such as those found in the microwave region. Therefore, a clear need to aid the knowledge base and investigate MMW radar phenomenology has been undertaken in this thesis. The research this thesis documents concerns designing, building and, fielding a distributed aperture array W-band (MMW) radar system. This thesis details incrementing the current fielded radar system capability from mono-static to bi-static imaging configurations. An improved method for calibrating the radar system resulting in higher quality imagery is also documented. The defined radar system was designed with the goal of performing multi-static Tomographic imaging. The research covered in this thesis is the first step toward incrementing the fielded system to full maturity.

Committee: Michael Wicks (Committee Chair); Lorenzo Lo Monte (Committee Member); Howard Evans (Committee Member); Robert Penno (Committee Member); Andrew Bogle (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy, The Ohio State University, 2013, Electrical and Computer Engineering

Until recently, the functionality of radar systems has been built into the radar's analog hardware, resulting in radars which are inflexible and that can only be used for a specific application. Modern systems, however, driven by the ever increasing speed of processors and data converters - analog-to-digital (ADC) and digital-to-analog (DAC) - are transitioning toward software defined radar (SDR) systems. The advent of SDRs inevitably leads to the question of how their added flexibilities can best be leveraged. The work within this dissertation is motivated by joint radar and communication functionality. The main objective is to study and demonstrate the ability of radar systems to employ non-traditional, specifically, communication waveforms for remote sensing. A software defined radar (SDR) is developed. The SDR features a "closed loop" testbed interface accessible via Matlab m-code. Here, "closed-loop" means that data can be pulled from the SDR, processed, then used to select/adapt the waveform and settings of the SDR without human intervention, i.e. on the fly. The testbed interface is used to implement a joint radar and communication system which is capable of collecting and processing radar data, e.g. range-Doppler maps, while simultaneously communicating previously collected radar data. Simultaneous functionality is accomplished by interrogating with a wide band digital communication waveform which is modulated with the previously collected radar data. The joint system is used to empirically demonstrate the theoretical work on detection and change detection within this dissertation. Optimal detectors are developed for interrogation with communication waveforms. The optimal detector for a single target with known impulse response in white noise is known to be a thresholding of the output of a matched filter. Radar systems, however, often operate in multi-target environments; notably air-to-ground synthetic aperture radars. For such applic (open full item for complete abstract)

Committee: Emre Ertin (Advisor); Randolph Moses (Advisor); Chris Baker (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2016, Electrical Engineering

The goal of this research is to develop a method for affordable and reliable sampling and coherent processing of measurement data collected via a modified magnetron oscillator based marine radar system. Non-coherent low-priced marine radar systems offer limited surveillance in clutter rich environments as compared to more expensive and complex coherent solid state radar systems. The approach used herein leverages modern analog to digital converters (ADC) and field programmable gate array (FPGA) technology to affordably and effectively sample the radiated and received signals for further analysis using FFT-based Doppler processing or cross correlation analysis. Track processing of moving targets is fundamental to any advanced radar and is a further focus of this research. The marine radar hardware is modified to capture the transmit signal at the source, and the receive signal at the aperture, for processing via FPGAs. The receive pulse train is cross-correlated with the transmit pulse train reference to remove the uncertainties in the phase history of the collected data. This operation ultimately makes the radar fully coherent on receive. Once the receive signal is made coherent, classical Doppler processing is used to differentiate moving targets from clutter and electromagnetic interference. A real time system has been built on a board with ADCs, FPGAs, and a microprocessor. Mixing of the Transmit (TX) and the Receive (RX) signals, Fourier transform analysis, and Pulse Compression are all executed digitally in the FPGA whereas Doppler Processing is performed on the microprocessor. This paper presents the underlying principles of cohering signals on receive, and it will show a real-time implementation of such algorithms using FPGAs.

Committee: Michael Wicks PhD (Advisor); Lorenzo Lo Monte PhD (Committee Chair); Guru Subramanyam PhD (Committee Chair); Eric Balster PhD (Committee Chair) Subjects: Electrical Engineering; Engineering

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

Master of Science in Engineering (MSEgr), Wright State University, 2011, Electrical Engineering

Typical radar systems are limited to energy distribution characteristics that are range independent. However, operators are generally interested in obtaining information at particular ranges and discarding elsewhere. It seems appropriate then to attempt to put energy solely at the range(s) of interest, thus minimizing exposure to clutter, jammers and other range-dependent interferences sources. The frequency diverse array (FDA) can provide a mechanism to achieve range-dependent beamforming and the spatial energy distribution properties are investigated on transmit and receive for different architectures herein. While simplified FDA receive architectures have been explored, they exclude the return signals from transmitters that are not frequency matched. This practice neglects practical consideration in receiver implementation and has motivated research to formulate a design that includes all frequencies. We present several receiver architectures for a uniform linear FDA, and compare the processing chain and spatial patterns in order to formulate an argument for the most efficient design to maximize gain on target. It may also be desirable to beamsteer in higher dimensionalities than a linear array affords, thus, the transmit and receive concept is extended to a generic planar array. This new architecture allows 3-D beamsteering in angle and range while maintaining practicality. The spatial patterns that arise are extremely unique and afford the radar designer an additional degree of freedom to develop operational strategy. The ability to simultaneously acquire, track, image and protect assets is a requirement of future fielded systems. The FDA architecture intrinsically covers multiple diversity domains and, therefore, naturally lends it self to a multi-mission, multi-mode adar scheme. A multiple beam technique that uses coding is suggested to advance this notion.

Committee: Brian Rigling PhD (Advisor); Douglas Petkie PhD (Committee Member); Fred Garber PhD (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2020, Electrical and Computer Engineering

This thesis will shown the development of an experimental radar testbed to test cognitive radar applications. The testbed is built upon the Universal Software Radio Peripheral (USRP) X-310, which is a member of a line of software de_ned radio (SDR)s available from Ettus. The testbed can, therefore, be kept at low cost and hence affordable to academia and smaller research projects. Experimental activities presented the system's detection range for a large airliner as approximately 5.5km, for a small aircraft as approximately 2.8km, and a detection range of a small unmanned aerial vehicle (UAV) as more than 350m. An experiment was conducted tracking a small UAV, illustrating that the system is capable of tracking a small target. A method for absolute radar cross section (RCS) calibration and channel characterization is shown. The thesis has also shown the development of a novel cost function for track update interval control using a fully adaptive radar (FAR) framework. An algorithm has been developed for a tracking system with track update interval control using the cost function developed. A simulator written in Matlab tested the algorithm in a set of scenarios. The cognitive radar (CR) experimental testbed was used as a radar system with the adaptive track update interval algorithm implemented. The algorithm was tested through a simple scenario of a UAV ying between two waypoints, and the waypoints were radially to the radar system. Finally, the thesis shows an adaptive beam scheduling method for radar surveillance, where a target present/absent function is used in a FAR framework to increase the cumulative detection performance. Simulation results for multiple maneuvering targets are shown, where the cumulative detection performance for both targets is close to a 100% over the raster beam scheduling method. A many target scenario is shown as well, where the cumulative detection performance is lower than for one or two targets; however, the performance is st (open full item for complete abstract)

Committee: Graeme Smith (Advisor); Joel Johnson (Advisor); Robert Burkholder (Committee Member); Emre Ertin (Committee Member); Daniel Wozniak (Committee Member) Subjects: Electrical Engineering

Master of Science in Electrical Engineering (MSEE), Wright State University, 2018, Electrical Engineering

This thesis considers the use of synthetic aperture radar (SAR) to provide absolute platform position information in scenarios where GPS signals may be degraded, jammed, or spoofed. Two algorithms are presented, and both leverage known 3D ground structure in an area of interest, e.g. provided by LIDAR data, to provide georeferenced position information to airborne SAR platforms. The first approach is based on the wide-aperture layover properties of elevated reflectors, while the second approach is based on correlating backprojected imagery with digital elevation imagery. Both of these approaches constitute the system we have designated: SARNAV. Building on 3D backprojection, localization solutions result from non-convex optimization problems based on image sharpness or correlation measures. Results using measured GOTCHA data demonstrate localization errors of only a few meters with initial uncertainty regions as large as 16 km^2. Finally, the system is incorporated into a Kalman filter tracker, where periodic SARNAV updates could be used to correct drift from an inertial navigation system. With measured data, the system was able to track the true position along the route within a few meters of error.

Committee: Joshua Ash Ph.D. (Advisor); Michael Saville Ph.D., P.E. (Committee Member); Arnab Shaw Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2018, Electrical and Computer Engineering

The goal of this research is to develop a method that allows for the processing of bistatic modified non-coherent marine radar's signals coherently, for the purpose of the warfare and electronic protection. Since the marine radar transmit signal is a non-coherent signal, it makes it difficult for the jammer to deceive the radar. Each marine radar is physically modified to work coherently and then configured to form bistatic radar. In this work, a method is presented for coherent processing of signals from a bistatic magnetron oscillator based marine radar. The feasibility of this approach was previously demonstrated for a monostatic radar through a hardware modification that allowed for capture of data and processing in PC. It is demonstrated here that operating two radars in this manner and combining their resulting signals allows for an improvement in overall detection and tracking. Our approach works by sampling the transmitted and received signals at each radar. Cross-correlations between all four combinations of transmitted and received signals are used to demonstrate the limits due to mutual interference in a bistatic/multistatic system of radars. This processing is successfully demonstrated in software, showing the potential for coherency between two marine radars. In general, bistatic coherent radars are very expensive, and this work provides a method for achieving the equivalent coherent performance using two modified non-coherent radar systems.

Committee: Michael Wicks PhD (Advisor) Subjects: Electrical Engineering

Doctor of Philosophy (PhD), Wright State University, 2017, Engineering PhD

Passive bistatic radar can be an attractive choice relative to monostatic radar because it provides the ability to operate covertly; immunity to jamming and interference; the ability to operate outside of traditional radar bands; and reduced cost. The benefits of noise waveforms versus classic radar waveforms such as linear frequency modulation (LFM) are discussed in the literature. Noise waveforms, with their thumbtack ambiguity functions, are ideal for use in non-cooperative passive radar. Since many digital waveforms are randomized to make their spectra approximately white, noise-like waveforms may be readily available for opportunistic use by non-cooperative passive radar receivers. For instance, the literature points out that digital television transmitters offer a powerful, well-defined signal with sufficient bandwidth for reasonable precision in range and are noise-like, thereby allowing for good, consistent range compression and Doppler estimation of targets. Much of the literature assumes that the transmitted noise (or noise-like) waveform is white (flat spectrum) over a finite bandwidth, and with good reason. However, some illuminators may emit waveforms that are not white. When the transmitted waveform's spectrum is colored (correlated), the cross-correlation process is likely to produce unacceptably high sidelobes. Meanwhile, LMS may produce more acceptable sidelobes. Until now, no theoretical expressions for the SNR at the output of the LMS family of algorithms existed in the literature for cases in which variants of the LMS algorithm are used to process colored Gaussian noise input data. The original contribution of this research is as follows. An equation is derived which predicts the theoretical output SNR when processing colored Gaussian noise input data using conventional LMS, valid at steady-state. Theoretical results have been corroborated by simulation results, and this contribution has been completed. The equation (open full item for complete abstract)

Committee: Brian Rigling Ph.D. (Committee Chair); Fred Garber Ph.D. (Committee Member); Arnab Shaw Ph.D. (Committee Member); Michael Temple Ph.D. (Committee Member); Muralidhar Rangaswamy Ph.D. (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

Master of Science, Miami University, 2016, Computational Science and Engineering

This thesis presents theoretical analysis and experimental evidence to demonstrate the feasibility of an ultra-wideband (UWB) software-defined radar sensor (SDRS) for detection and identification of targets in a three-dimensional target scene. Orthogonal frequency division multiplexing (OFDM) is used to modulate the UWB SDRS signal. Spectral responses from targets at each of the OFDM sub-carriers are investigated experimentally and the results are used to develop frequency prolfies as the viewing angle of the SDRS changes with respect to the target. These profiles are then used to perform detection and identification of targets when characteristics of those targets are known. Simulations and experiments are presented to illustrate the effectiveness of the detection and identification algorithms which are the primary focus of this thesis.

Committee: Dmitriy Garmatyuk Dr. (Advisor); Chi-Hao Cheng Dr. (Committee Member); Mark Scott Dr. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2016, Electrical Engineering

Radio Frequency (RF) Tomography is a mathematical process of 3D image reconstruction from a measurement using a multistatic distribution of transmitters and receivers. The geometric diversity of these elements increases the information in the measurements. The process of determining the permittivity and conductivity profile in the measurement domain, and, therefore, the shape of the target, from the scattered field measurements, is an inverse problem. To solve this problem, under conventional methods such as the Born approximation, we use the principles of linear scattering to determine a linear relationship between measured returns and target shape. The Born approximation is valid if the scatterer is small and does not interact strongly with other objects. However, strong scatterers within the domain may generate sidelobes masking weaker returns. This masking, in conjunction with multipath effects, may result in loss of features and subsequent failure to identify a target. In this research, a novel method is proposed to increase overall image quality and extend the capabilities of RF tomography by modeling the strong scatterers in the measurement domain as dipoles that behave as secondary sources (transmitters). Unlike conventional methods, the dipole model reduces the effects of the sidelobes from the strong scatterers and exploits the multipath of multiple targets or complex shapes. The multipath phenomena contains more information about the targets permitting illumination in the shadowed region and an increase to the radar aperture length. The electromagnetic characteristics for each modeled dipole are estimated by representing the cells in the measurement domain's image. The eigenvalue and eigenvector from each cell represent the phase and magnitude for the modeled dipole and also the spatial orientation of the target. The process of modeling large scatterers as dipoles can be iterated, addressing one strong scatterer at a time. This method effectively s (open full item for complete abstract)

Committee: Michael Wicks (Advisor); Keigo Hirakawa (Committee Member); John Loomis (Committee Member); Lorenzo Lo Monte (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (PhD), Wright State University, 2016, Engineering PhD

Today's radars face an ever increasingly complex operational environment, intensified by the numerous types of mission/modes, number and type of targets, non-homogenous clutter and active interferers in the scene. Thus, the ability to adapt ones transmit waveform, to optimally suit the needs for a particular radar tasking and environment, becomes mandatory. This requirement brings with it a host of challenges to implement including the basic decision of what to transmit. In this dissertation, we discuss six original contributions, including the development of performance prediction models for constrained radar waveforms, that aid in the decision making process of an adaptive radar in selecting what to transmit. It is critical that the algorithms and performance prediction models developed be robust to varying radio frequency interference (RFI) environments. However, the current literature only provides toy examples not suitable in representing real-world interference. Therefore, we develop and validate two new power spectral density (PSD) models for interference and noise, derived from measured data, that allow us to ascertain the effectiveness of an algorithm under varying conditions. We then investigate the signal-to-interference-and-noise ratio (SINR) performance for a multi-constrained waveform design in the presence of colored interference. We set-up and numerically solve two optimization problems that maximize the SINR while applying a novel waveform design technique that requires the signal be an ordered subset of eigenvectors of the interference and noise covariance matrix. The significance of this work is the observation of the non-linearity in the SINR performance as a function of the constraints. This inspires the development of performance prediction models to obtain a greater understanding of the impact practical constraints have on the SINR. Building upon these results, we derive two new performance models, one for the constraine (open full item for complete abstract)

Committee: Brian Rigling Ph.D. (Advisor); Muralidhar Rangaswamy Ph.D. (Committee Member); Christopher Baker Ph.D. (Committee Member); Fred Garber Ph.D. (Committee Member); Wu Zhiqiang Ph.D. (Committee Member) Subjects: Aerospace Engineering; Electrical Engineering

Master of Science, The Ohio State University, 2008, Electrical Engineering

Radar imaging techniques for building exteriors have been extensively studied over the past several years. However, through-wall imaging presents us with several challenges. Among them are (1) wall penetrating signal attenuation and distortion and (2) demand of adequate modeling techniques. This thesis begins by reviewing near zone imaging algorithms based on scattering centers, and proceeds with the adaptation of new heuristic edge and corner diffraction coefficients for dielectric wedges using transmission and reflection characteristics of multilayered slab to ensure shadow boundary continuity. The high frequency building characterizing technique is then validated against experimental radar measurement. By using the Ohio State University NEC-BSC code upgraded with the new diffraction coefficients, it is demonstrated that polarization and bistatic SAR can be used in detecting or avoiding certain building features and hidden objects. To improve interior wall and object imaging, the CLEAN algorithm is employed to remove exterior wall contributions using pre-computed scattering signatures.

Committee: John Volakis (Advisor) Subjects:

Master of Science (MS), Ohio University, 2011, Electrical Engineering (Engineering and Technology)

Human safety is one of the highest priorities in the automotive industry. The demands made for reliable safety systems have been increasing tremendously in the past decade. The radar sensors used for safety systems should be capable of detecting not only automobiles but also motorcycles, bicycles, pedestrians, roadside objects and any other obstacles the vehicle may come in contact with. This thesis investigates several performance aspects and test procedures for a 77 GHz long range radar sensor with different test target objects. This assessment helps to investigate the potential to use these radar sensors for obstacle detection and/or avoidance for smaller objects like bicycles, humans, traffic barrels, 4” poles, metal sheets, and also for bigger objects like vans, motorcycles, aircraft, etc. For these purposes, different test cases were developed to evaluate the performance. The different test cases used to test a 77 GHz radar sensor includes: finding maximum range, range accuracy, finding maximum field of view, detection (& separation) of two target objects (similar & different) at different radial distances, and maximum range for detecting an aircraft. Observations were made with the radar sensor mounted on a moving cart and the measurements were logged. The results from these tests will provide insight into analyzing the possibilities and limitations of these radar sensors for different applications. The tests were successfully conducted on a flat, open field at Ohio University Airport, Albany, OH.

Committee: Chris Bartone PhD, P.E. (Advisor); Jeffrey Dill PhD (Committee Member); Bryan Riley PhD, PMP (Committee Member); William Kaufman PhD (Committee Member) Subjects: Automotive Engineering; Electrical Engineering

Master of Science in Electrical Engineering (MSEE), Wright State University, 2024, Electrical Engineering

This research illustrates a high-security wireless communication method using a joint radar/communication waveform, addressing the vulnerability of traditional low probability of detection (LPD) waveforms to hostile receiver detection via cyclostationary processing (CSP). To mitigate this risk, RF steganography is used, concealing communication signals within linear frequency modulation (LFM) radar signals. The method integrates reduced phase-shift keying (RPSK) modulation and variable symbol duration, ensuring secure transmission while evading detection. Implementation is validated through software-defined radios (SDRs), demonstrating effectiveness in covert communication scenarios. Results include analysis of message reception and cyclostationary features, highlighting the method's ability to conceal messages from hostile receivers. Challenges encountered are discussed, with suggestions for future enhancements to improve real-world applicability.

Committee: Zhiqiang Wu Ph.D. (Advisor); Xiaodong Zhang Ph.D. (Committee Member); Bin Wang Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2023, Electrical and Computer Engineering

Rough surface scattering is an essential aspect of modern remote sensing research, as virtually all real-world surfaces exhibit some degree of roughness whose effects cannot be adequately accounted for using simple planar surfaces. However, knowledge of what each rough surface model is capable of is critical, as choosing an appropriate model will aid in providing accurate results while minimizing the computational cost incurred. To explore these capabilities, four studies were conducted to assess how various rough surface scattering models fare in scenarios of current interest to the remote sensing community. The first two studies involve the Kirchhoff approximation (KA), with the first study assessing its applicability when the normalized coherent reflected field (which the KA is commonly used to model) is -20dB or lower, and the second study comparing it to a second-order correction term based on the second-order small slope approximation (SSA2) for ocean surface scattering. The first study shows that the KA continues to be applicable for such low amplitude cases, and the second study shows that the second-order correction shows no marked improvement over the base KA overall. The third study uses the SSA2 to validate retrieved zero-Doppler delay waveforms as part of a campaign to explore off-specular ocean scattering, and found the model waveforms to match the retrieved waveforms well in most cases considered. The fourth and final study uses simulated SAR imagery to determine under what conditions a monostatic radar system will observe the same surface scattering as a bistatic radar system, and revealed that cases with near-normal incidence angles and minor roughness yield the best agreement, with effects such as shadowing and multiple reflections accounting for most of the disagreements.

Committee: Joel Johnson (Advisor); Fernando Teixeira (Committee Member); Robert Burkholder (Committee Member) Subjects: Electrical Engineering; Electromagnetics; Physics; Remote Sensing

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

Over the past few decades, the ubiquity of radio-frequency (RF) devices has improved connectivity and productivity in our lives through wireless communication and sensing technologies. To this end, vehicle-to-everything (V2X) communication and vehicular radar imaging technologies have become the key enablers of Intelligent Transportation Systems (ITS) to promote safety, automation, and coordination in traffic. To enable V2X communication, a limited amount of bandwidth in the 5.9 GHz spectrum is dedicated to vehicles for the exchange of basic safety messages with low latency. However, with the large-scale deployment of connected vehicles, the V2X-dedicated band faces the spectrum scarcity problem that lowers the reliability of vehicular communication. The scarcity of dedicated spectrum also limits the feasibility and capabilities of more advanced vehicular applications that rely on broadband communication. Besides, up to 4 GHz of contiguous bandwidth is allocated as the vehicular radar spectrum that is dedicated solely to vehicles in the 76-81 GHz millimeter-wave (mmWave) bands. To supplement V2X communication, the under-utilized vehicular radar spectrum can be leveraged by joint radar-communication (JRC) systems. The objective of JRC is to perform both data transmission and radar imaging using the same \textit{joint} waveform and transceiver hardware. In this dissertation, we investigate transmission optimization and scheduling approaches to enable vehicular JRC in mmWave bands using adaptive orthogonal frequency-division multiplexing (OFDM). First, we study the joint waveform design problem for wideband vehicular JRC. By exploiting the frequency-selectivity in wideband channels, we adaptively design subcarrier coefficients of OFDM to achieve long-range detection and communication performance. We show that the problem is a non-convex quadratically constrained quadratic programming (QCQP), which is NP-hard. As an alternative to existing approaches, we propose time (open full item for complete abstract)

Committee: Eylem Ekici (Advisor); Ness Shroff (Committee Member); Can Emre Koksal (Committee Member) Subjects: Computer Engineering; Electrical Engineering

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

Cloud profiling radars (CPRs) are designed to illuminate atmospheric clouds and estimate their water content and internal structure. Satellite based CPRs offer several advantages and have already been used to significantly improve scientific understanding of meteorology generally, but the physical nature of clouds makes them a challenging target to observe without large, expensive instruments. Adaptive remote sensing has the potential to enable smaller, cheaper systems to produce useful measurements alongside their larger predecessors. A Matlab simulation has been developed to investigate the benefits of adaptive remote sensing in the CPR use case. This simulation has the ability to adapt the radar's pulse repetition frequency (PRF) in response to observed cloud column height as well as automatically identify and terminate low precision measurements to manage resources. The simulation has been tested using synthetic data derived from NASA's GEOS5 system and real data derived from CloudSat's operational history. The simulation has shown that adaptive remote sensing is capable of achieving resource savings and quality improvement in the CPR case which would make the use of highly resource constrained platforms for the CPR use case more feasible.

Committee: Johnson Joel (Advisor); Ertin Emre (Committee Member); Brian Slater (Committee Member); Fernando Teixiera (Committee Member) Subjects: Atmospheric Sciences; Electrical Engineering; Engineering; Remote Sensing

Master of Science, The Ohio State University, 2021, Aerospace Engineering

Small unmanned aerial systems (sUAS) pose an ever-increasing threat to low-altitude aircraft, particularly those unequipped with automatic dependent surveillance – broadcast (ADS-B) trackers. Surveillance and tracking systems must be developed to find these low-flying aircraft and alert remote pilots to their location, especially for beyond visual line of sight (BVLOS) operations. Radar is introduced as one such means for this surveillance that is independent of the sUAS and aircraft systems operating within the surveillance volume. The downside to radar at such extremely low-altitude environments, however, is the large volume of radar “clutter” returns that arise from ground vehicles, weather, birds, trees, etc. For radar to be useful as the surveillance method of choice, these clutter returns must be filtered out of the radar feeds so remote pilots have the clearest and most complete picture of the surrounding airspace possible. This work proposes a neural network classifier as the tool of choice in decluttering the radar feeds. A neural network algorithm requires no known models of aircraft and clutter behavior inside the surveillance volume. The only requirement for training the model is a labeled set of radar returns spanning a few days. This thesis describes the process for building this classification model, while also providing a method for labeling radar data, building a feature set for the neural network to learn from, and supplementing training data due to the lack of physical radar tracks for specific use cases. The proposed model in this work is shown to reduce clutter in the radar feeds by over 80% in most cases, while correctly identifying more than 94% of aircraft. This research lays the groundwork for future real-time classification algorithms and provides insight and suggestions for means of future improvement.

Committee: Jeffrey Bons (Advisor); Matthew McCrink (Committee Member); Jim Gregory (Committee Member) Subjects: Aerospace Engineering