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 (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

Doctor of Philosophy, The Ohio State University, 2016, Electrical and Computer Engineering

Multiple-input multiple-output (MIMO) radar is an emerging set of technologies designed to extend the capabilities of multi-channel radar systems. While conventional radar architectures emphasize the use of antenna array beamforming to maximize real-time power on target, MIMO radar systems instead attempt to preserve some degree of independence between their received signals and to exploit this expanded matrix of target measurements in the signal-processing domain. Specifically the use of sparse “virtual” antenna arrays may allow MIMO radars to achieve gains over traditional multi-channel systems by post-processing diverse received signals to implement both transmit and receive beamforming at all points of interest within a given scene. MIMO architectures have been widely examined for use in radar target detection, but these systems may yet be ideally suited to real and synthetic aperture radar imaging applications where their proposed benefits include improved resolutions, expanded area coverage, novel modes of operation, and a reduction in hardware size, weight, and cost. While MIMO radar's theoretical benefits have been well established in the literature, its practical limitations have not received great attention thus far. The effective use of MIMO radar techniques requires a diversity of signals, and to date almost all MIMO system demonstrations have made use of time-staggered transmission to satisfy this requirement. Doing so is reliable but can be prohibitively slow. Waveform-diverse systems have been proposed as an alternative in which multiple, independent waveforms are broadcast simultaneously over a common bandwidth and separated on receive using signal processing. Operating in this way is much faster than its time-diverse equivalent, but finding a set of suitable waveforms for this technique has proven to be a difficult problem. In light of this, many have questioned the practicality of MIMO radar imaging and whether or not its theoretical ben (open full item for complete abstract)

Committee: Joel Johnson (Advisor); Robert Burkholder (Committee Member); Emre Ertin (Committee Member) Subjects: Electrical Engineering; Remote Sensing

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

Master of Science, The Ohio State University, 2010, Electrical and Computer Engineering

There has been tremendous advancement of digital technology over the past years. With the availability of high speed analog to digital (A/D) and digital to analog (D/A) converters, digital signal processor (DSP) enabled real-time processors with fast field programmable gate arrays (FPGAs), it is now possible to build a software defined radar (SDR), which is capable of switching transmit-waveforms adaptively on-the-fly at each pulse repetition interval (PRI). Using SDR it is possible to improve resolution by transmitting waveforms which are adapted to the scene. It is also possible to perform multi-mode operation such as combination of tracking and imaging. To exploit SDR's huge capability in terms of adaptive waveform selection in an efficient way, it is now necessary to study different waveforms and build optimum waveform scheduling techniques for different radar operations. In this thesis we consider the problem of waveform scheduling for multiple range-Doppler radars operating collaboratively for target tracking. First, we consider the canonical case of collocated radar platforms, each capable of choosing a waveform at every PRI from a fixed chirp waveform library. We assume the radars fuse the information that they obtain from their measurements after each chirp pulse in a Kalman Filter tracking framework to obtain the posterior distribution of the target state. The radars then use the resulting distribution to jointly select their next waveform set. Our results extend previous work on information theoretic waveform-scheduling proposed for single range-Doppler radar to multiple radars interrogating the same target. We show that the radar platforms must employ a mixture of minimum and maximum chirp-rates supported by the radar hardware to maximize mutual information between the target state and measurements. Finally, the results are extended to an arbitrary number of radars located at arbitrary positions in 2-D plane. Thus, we show that to achieve improved radar (open full item for complete abstract)

Committee: Emre Ertin PhD (Advisor); Lee Potter PhD (Advisor) Subjects: Electrical 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

PhD, University of Cincinnati, 2019, Arts and Sciences: Geography

Thermokarst lakes are the most conspicuous features in the Arctic coastal regions that cover roughly 15% - 40 % percent of the area. Those lakes play as a critical niche in the local environment system and provide habitats for a great number of species. In the context of global warming, lakes are experiencing dramatic changes in recent decades. The lake water level and the snow cover atop the ice in the winter are two sensitive indicators of the local and global climate change. Monitoring the variations in lake water level and snow accumulation in Arctic regions could provide more insights of the global climate change and facilitate our understanding of their influences on local hydrological and ecological systems. However, there are very rare in situ observations of lake water levels and lake snow accumulations for the Arctic regions due to the remote locations and also the harsh environmental conditions. Satellite radar and laser altimetry measures elevation profiles of Earth's surface at the global scale and offers an alternative to achieve the purpose. Most previous studies have focused on the application of satellite radar and laser altimetry on lakes at low or middle latitudes, with few of them discussing the applicability of these data to high-latitude lakes. In this research, I explored the capability of satellite radar and laser altimetry missions to monitor lake water levels and snow accumulation on frozen lakes in the Arctic coastal regions. The performances of Sentinel-3, the most recent satellite radar altimetry, on the retrieval of lake water levels were assessed particularly for high-latitude ice-covered lakes. The results showed that lake ice can greatly reduce the accuracy of Sentinel-3 observations. I developed a new empirical retracking algorithm that significantly improves the measurements and provide more reliable and consistent water level estimates for the ice-covered lakes. I examined the performances of ICESat/GLAS, the first and until now (open full item for complete abstract)

Committee: Hongxing Liu Ph.D. (Committee Chair); Richard Beck Ph.D. (Committee Member); Kenneth Hinkel Ph.D. (Committee Member); Emily Kang Ph.D. (Committee Member); Tomasz Stepinski Ph.D. (Committee Member) Subjects: Geography

Doctor of Philosophy, The Ohio State University, 2014, Electrical and Computer Engineering

Radar waveform design is an active research area for decades. With the advent of advanced digital signal processing, high speed computing, high frequency electronics, and solid state power amplifiers, emerging radar systems (such as UWB radar, multiple-input and multiple-output (MIMO) radar, cognitive radar, etc.) are expecting more from their waveforms. Taking advantage of the new techniques, scientists and engineers are able to implement new waveforms to achieve significantly better performance for conventional radar systems, namely target detection including range, speed, and shape. The objective of this dissertation is to exploit a practical way to build flexible waveforms for the modern radar. On the other hand, conventional radar systems detect targets or pixels of an area individually. Each target or pixel generates a set of data in real-time, which must be recorded for off-line processing. When the number of elements is increased, phased array radar is able to generate narrow beams, which can detect more targets or cover larger areas for data collection in high definition. The disadvantage is the increased time in sensing since narrow beams need more time to cover the same area than wider beams. To address this issue, the sensing mechanism needs to be studied. The objective of this dissertation is to exploit a new sensing mechanism, named transform sensing, to cover wider areas, tracking more moving objects, and providing high resolution of the target area with limited times of sensing. Because the waveform design and transform sensing in this dissertation are all based on wavelets, the dissertation introduces the wavelet basics. Then the wavelet based waveform is presented. This waveform is generated by concatenating wavelet packets, and can suppress range sidelobes more effectively than the tranditional Linear Frequency Modulated (LFM) waveform. In addition, the wavelet based waveform can de-couple its envelope and carrier for range and velocity (open full item for complete abstract)

Committee: Yuan Zheng (Advisor); Chris Baker (Committee Member); Chi-Chih Chen (Committee Member) Subjects: Electrical Engineering; Remote Sensing

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

With more users populating the RF spectrum and hence less available contiguous bandwidth, radar and communication waveforms are slowly forced to become more efficient at using their available frequencies. Two scenarios are considered: operation in a colored interference environment and operation in discontiguous spectral bands. Unconstrained algorithms for designing transmit waveforms and receive filters are evaluated, wherein varying a convex weight trades performance between spectral flatness and side lobe levels. An empirical study provides performance bounds for constrained radar waveform designs for an instantiation of the interference spectrum. Closed-form predictions for integrated sidelobe ratio (ISLR) and peak-to-sidelobe ratio (PSLR) for radar waveforms designed to operate in discontiguous spectral bands are derived and validated against two spectrally-disjoint waveform designs. These spectrally-disjoint waveform designs must also consider constraints imposed by hardware, such as modulus and phase restrictions. In the final part of this thesis, four spectrally-disjoint waveform designs are subjected to hardware-in-the-loop tests. Experimental results are shown and compared to computer simulations.

Committee: Brian Rigling PhD (Advisor); Fred Garber PhD (Committee Member); Zhiqiang Wu PhD (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (PhD), Wright State University, 2011, Engineering PhD

Modern frequency modulated continuous wave (FMCW) radar technology provides the ability to modify the system transmission frequency as a function of time, which in turn provides the ability to generate multiple output waveforms from a single radar unit. Current low-power multi-waveform FMCW radar techniques lack the ability to reliably associate measurements from the various waveform sections in the presence of multiple targets and multiple false detections within the field-of-view. Two approaches are developed here to address this problem. The first approach takes advantage of the relationships between the waveform segments to generate a weighting function for candidate combinations of measurements from the waveform sections. This weighting function is then used to choose the best candidate combinations to form polar-coordinate measurements. Simulations show that this approach provides a ten to twenty percent increase in the probability of correct association over the current approach while reducing the number of false alarms in generated in the process, but still fails to form a measurement if a detection form a waveform section is missing. The second approach models the multi-waveform FMCW radar as a set of independent sensors and uses distributed data fusion to fuse estimates from those individual sensors within a tracking structure. Tracking in this approach is performed directly with the raw frequency and angle measurements from the waveform segments. This removes the need for data association between the measurements from the individual waveform segments. A distributed data fusion model is used again to modify the radar tracking systems to include a video sensor to provide additional angular and identification information into the system. The combination of the radar and vision sensors, as an end result, provides an enhanced roadside tracking system.

Committee: Lang Hong PhD (Advisor); Michael Temple PhD (Committee Member); Kefu Xue PhD (Committee Member); Zhiqiang (John) Wu PhD (Committee Member); Arthur Goshtasby PhD (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy (PhD), Wright State University, 2009, Engineering PhD

We consider the design of radar systems that are capable of using knowledge of their interference environment to dynamically design transmit waveforms that afford optimum signal-to-interference-plus-noise ratio while satisfying modulus and ambiguity function constraints. We begin by establishing the inextricable nature of modulus constraints in the waveform optimization problem. We then extend the state of the art in waveform optimization to accommodate these constraints. This is done by solving a secondary optimization problem using the method of alternating projections. We demonstrate that this approach can be a computationally efficient alternative to dynamic programming methods. We then consider the multiple-target detection problem, which is the basis for introducing ambiguity function constraints into the waveform design process. We formulate the waveform optimization problem for several receiver architectures, and solve these problems using sequential quadratic programming and interior point methods. Finally, we address the need for a more computationally tractable approach by considering a number of suboptimal formulations. This includes a novel formulation based on a parametrization of nonlinear frequency modulation.

Committee: Brian Rigling PhD (Advisor); Kefu Xue PhD (Committee Member); Zhiqiang Wu PhD (Committee Member); Michael Bryant PhD (Committee Member); Mark Oxley PhD (Committee Member) Subjects: Electrical Engineering; Engineering