Defense Notices
All students and faculty are welcome to attend the final defense of EECS graduate students completing their M.S. or Ph.D. degrees. Defense notices for M.S./Ph.D. presentations for this year and several previous years are listed below in reverse chronological order.
Students who are nearing the completion of their M.S./Ph.D. research should schedule their final defenses through the EECS graduate office at least THREE WEEKS PRIOR to their presentation date so that there is time to complete the degree requirements check, and post the presentation announcement online.
Upcoming Defense Notices
Elizabeth Wyss
A New Frontier for Software Security: Diving Deep into npmWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Drew Davidson, ChairAlex Bardas
Fengjun Li
Bo Luo
J. Walker
Abstract
Open-source package managers (e.g., npm for Node.js) have become an established component of modern software development. Rather than creating applications from scratch, developers may employ modular software dependencies and frameworks--called packages--to serve as building blocks for writing larger applications. Package managers make this process easy. With a simple command line directive, developers are able to quickly fetch and install packages across vast open-source repositories. npm--the largest of such repositories--alone hosts millions of unique packages and serves billions of package downloads each week.
However, the widespread code sharing resulting from open-source package managers also presents novel security implications. Vulnerable or malicious code hiding deep within package dependency trees can be leveraged downstream to attack both software developers and the end-users of their applications. This downstream flow of software dependencies--dubbed the software supply chain--is critical to secure.
This research provides a deep dive into the npm-centric software supply chain, exploring distinctive phenomena that impact its overall security and usability. Such factors include (i) hidden code clones--which may stealthily propagate known vulnerabilities, (ii) install-time attacks enabled by unmediated installation scripts, (iii) hard-coded URLs residing in package code, (iv) the impacts of open-source development practices, (v) package compromise via malicious updates, (vi) spammers disseminating phishing links within package metadata, and (vii) abuse of cryptocurrency protocols designed to reward the creators of high-impact packages. For each facet, tooling is presented to identify and/or mitigate potential security impacts. Ultimately, it is our hope that this research fosters greater awareness, deeper understanding, and further efforts to forge a new frontier for the security of modern software supply chains.
Alfred Fontes
Optimization and Trade-Space Analysis of Pulsed Radar-Communication Waveforms using Constant Envelope ModulationsWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Patrick McCormick, ChairShannon Blunt
Jonathan Owen
Abstract
Dual function radar communications (DFRC) is a method of co-designing a single radio frequency system to perform simultaneous radar and communications service. DFRC is ultimately a compromise between radar sensing performance and communications data throughput due to the conflicting requirements between the sensing and information-bearing signals.
A novel waveform-based DFRC approach is phase attached radar communications (PARC), where a communications signal is embedded onto a radar pulse via the phase modulation between the two signals. The PARC framework is used here in a new waveform design technique that designs the radar component of a PARC signal to match the PARC DFRC waveform expected power spectral density (PSD) to a desired spectral template. This provides better control over the PARC signal spectrum, which mitigates the issue of PARC radar performance degradation from spectral growth due to the communications signal.
The characteristics of optimized PARC waveforms are then analyzed to establish a trade-space between radar and communications performance within a PARC DFRC scenario. This is done by sampling the DFRC trade-space continuum with waveforms that contain a varying degree of communications bandwidth, from a pure radar waveform (no embedded communications) to a pure communications waveform (no radar component). Radar performance, which is degraded by range sidelobe modulation (RSM) from the communications signal randomness, is measured from the PARC signal variance across pulses; data throughput is established as the communications performance metric. Comparing the values of these two measures as a function of communications symbol rate explores the trade-offs in performance between radar and communications with optimized PARC waveforms.
Arin Dutta
Performance Analysis of Distributed Raman Amplification with Different Pumping ConfigurationsWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Rongqing Hui, ChairMorteza Hashemi
Rachel Jarvis
Alessandro Salandrino
Hui Zhao
Abstract
As internet services like high-definition videos, cloud computing, and artificial intelligence keep growing, optical networks need to keep up with the demand for more capacity. Optical amplifiers play a crucial role in offsetting fiber loss and enabling long-distance wavelength division multiplexing (WDM) transmission in high-capacity systems. Various methods have been proposed to enhance the capacity and reach of fiber communication systems, including advanced modulation formats, dense wavelength division multiplexing (DWDM) over ultra-wide bands, space-division multiplexing, and high-performance digital signal processing (DSP) technologies. To maintain higher data rates along with maximizing the spectral efficiency of multi-level modulated signals, a higher Optical Signal-to-Noise Ratio (OSNR) is necessary. Despite advancements in coherent optical communication systems, the spectral efficiency of multi-level modulated signals is ultimately constrained by fiber nonlinearity. Raman amplification is an attractive solution for wide-band amplification with low noise figures in multi-band systems.
Distributed Raman Amplification (DRA) have been deployed in recent high-capacity transmission experiments to achieve a relatively flat signal power distribution along the optical path and offers the unique advantage of using conventional low-loss silica fibers as the gain medium, effectively transforming passive optical fibers into active or amplifying waveguides. Also, DRA provides gain at any wavelength by selecting the appropriate pump wavelength, enabling operation in signal bands outside the Erbium doped fiber amplifier (EDFA) bands. Forward (FW) Raman pumping configuration in DRA can be adopted to further improve the DRA performance as it is more efficient in OSNR improvement because the optical noise is generated near the beginning of the fiber span and attenuated along the fiber. Dual-order FW pumping scheme helps to reduce the non-linear effect of the optical signal and improves OSNR by more uniformly distributing the Raman gain along the transmission span.
The major concern with Forward Distributed Raman Amplification (FW DRA) is the fluctuation in pump power, known as relative intensity noise (RIN), which transfers from the pump laser to both the intensity and phase of the transmitted optical signal as they propagate in the same direction. Additionally, another concern of FW DRA is the rise in signal optical power near the start of the fiber span, leading to an increase in the non-linear phase shift of the signal. These factors, including RIN transfer-induced noise and non-linear noise, contribute to the degradation of system performance in FW DRA systems at the receiver.
As the performance of DRA with backward pumping is well understood with relatively low impact of RIN transfer, our research is focused on the FW pumping configuration, and is intended to provide a comprehensive analysis on the system performance impact of dual order FW Raman pumping, including signal intensity and phase noise induced by the RINs of both 1st and the 2nd order pump lasers, as well as the impacts of linear and nonlinear noise. The efficiencies of pump RIN to signal intensity and phase noise transfer are theoretically analyzed and experimentally verified by applying a shallow intensity modulation to the pump laser to mimic the RIN. The results indicate that the efficiency of the 2nd order pump RIN to signal phase noise transfer can be more than 2 orders of magnitude higher than that from the 1st order pump. Then the performance of the dual order FW Raman configurations is compared with that of single order Raman pumping to understand trade-offs of system parameters. The nonlinear interference (NLI) noise is analyzed to study the overall OSNR improvement when employing a 2nd order Raman pump. Finally, a DWDM system with 16-QAM modulation is used as an example to investigate the benefit of DRA with dual order Raman pumping and with different pump RIN levels. We also consider a DRA system using a 1st order incoherent pump together with a 2nd order coherent pump. Although dual order FW pumping corresponds to a slight increase of linear amplified spontaneous emission (ASE) compared to using only a 1st order pump, its major advantage comes from the reduction of nonlinear interference noise in a DWDM system. Because the RIN of the 2nd order pump has much higher impact than that of the 1st order pump, there should be more stringent requirement on the RIN of the 2nd order pump laser when dual order FW pumping scheme is used for DRA for efficient fiber-optic communication. Also, the result of system performance analysis reveals that higher baud rate systems, like those operating at 100Gbaud, are less affected by pump laser RIN due to the low-pass characteristics of the transfer of pump RIN to signal phase noise.
Audrey Mockenhaupt
Using Dual Function Radar Communication Waveforms for Synthetic Aperture Radar Automatic Target RecognitionWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Patrick McCormick, ChairShannon Blunt
Jon Owen
Abstract
Pending.
Rich Simeon
Delay-Doppler Channel Estimation for High-Speed Aeronautical Mobile Telemetry ApplicationsWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Erik Perrins, ChairShannon Blunt
Morteza Hashemi
Jim Stiles
Craig McLaughlin
Abstract
The next generation of digital communications systems aims to operate in high-Doppler environments such as high-speed trains and non-terrestrial networks that utilize satellites in low-Earth orbit. Current generation systems use Orthogonal Frequency Division Multiplexing modulation which is known to suffer from inter-carrier interference (ICI) when different channel paths have dissimilar Doppler shifts.
A new Orthogonal Time Frequency Space (OTFS) modulation (also known as Delay-Doppler modulation) is proposed as a candidate modulation for 6G networks that is resilient to ICI. To date, OTFS demodulation designs have focused on the use cases of popular urban terrestrial channel models where path delay spread is a fraction of the OTFS symbol duration. However, wireless wide-area networks that operate in the aeronautical mobile telemetry (AMT) space can have large path delay spreads due to reflections from distant geographic features. This presents problems for existing channel estimation techniques which assume a small maximum expected channel delay, since data transmission is paused to sound the channel by an amount equal to twice the maximum channel delay. The dropout in data contributes to a reduction in spectral efficiency.
Our research addresses OTFS limitations in the AMT use case. We start with an exemplary OTFS framework with parameters optimized for AMT. Following system design, we focus on two distinct areas to improve OTFS performance in the AMT environment. First we propose a new channel estimation technique using a pilot signal superimposed over data that can measure large delay spread channels with no penalty in spectral efficiency. A successive interference cancellation algorithm is used to iteratively improve channel estimates and jointly decode data. A second aspect of our research aims to equalize in delay-Doppler space. In the delay-Doppler paradigm, the rapid channel variations seen in the time-frequency domain is transformed into a sparse quasi-stationary channel in the delay-Doppler domain. We propose to use machine learning using Gaussian Process Regression to take advantage of the sparse and stationary channel and learn the channel parameters to compensate for the effects of fractional Doppler in which simpler channel estimation techniques cannot mitigate. Both areas of research can advance the robustness of OTFS across all communications systems.
Mohammad Ful Hossain Seikh
AAFIYA: Antenna Analysis in Frequency-domain for Impedance and Yield AssessmentWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Jim Stiles, ChairRachel Jarvis
Alessandro Salandrino
Abstract
This project presents AAFIYA (Antenna Analysis in Frequency-domain for Impedance and Yield Assessment), a modular Python toolkit developed to automate and streamline the characterization and analysis of radiofrequency (RF) antennas using both measurement and simulation data. Motivated by the need for reproducible, flexible, and publication-ready workflows in modern antenna research, AAFIYA provides comprehensive support for all major antenna metrics, including S-parameters, impedance, gain and beam patterns, polarization purity, and calibration-based yield estimation. The toolkit features robust data ingestion from standard formats (such as Touchstone files and beam pattern text files), vectorized computation of RF metrics, and high-quality plotting utilities suitable for scientific publication.
Validation was carried out using measurements from industry-standard electromagnetic anechoic chamber setups involving both Log Periodic Dipole Array (LPDA) reference antennas and Askaryan Radio Array (ARA) Bottom Vertically Polarized (BVPol) antennas, covering a frequency range of 50–1500 MHz. Key performance metrics, such as broadband impedance matching, S11 and S21 related calculations, 3D realized gain patterns, vector effective lengths, and cross-polarization ratio, were extracted and compared against full-wave electromagnetic simulations (using HFSS and WIPL-D). The results demonstrate close agreement between measurement and simulation, confirming the reliability of the workflow and calibration methodology.
AAFIYA’s open-source, extensible design enables rapid adaptation to new experiments and provides a foundation for future integration with machine learning and evolutionary optimization algorithms. This work not only delivers a validated toolkit for antenna research and pedagogy but also sets the stage for next-generation approaches in automated antenna design, optimization, and performance analysis.
Soumya Baddham
Battling Toxicity: A Comparative Analysis of Machine Learning Models for Content ModerationWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
David Johnson, ChairPrasad Kulkarni
Hongyang Sun
Abstract
With the exponential growth of user-generated content, online platforms face unprecedented challenges in moderating toxic and harmful comments. Due to this, Automated content moderation has emerged as a critical application of machine learning, enabling platforms to ensure user safety and maintain community standards. Despite its importance, challenges such as severe class imbalance, contextual ambiguity, and the diverse nature of toxic language often compromise moderation accuracy, leading to biased classification performance.
This project presents a comparative analysis of machine learning approaches for a Multi-Label Toxic Comment Classification System using the Toxic Comment Classification dataset from Kaggle. The study examines the performance of traditional algorithms, such as Logistic Regression, Random Forest, and XGBoost, alongside deep architectures, including Bi-LSTM, CNN-Bi-LSTM, and DistilBERT. The proposed approach utilizes word-level embeddings across all models and examines the effects of architectural enhancements, hyperparameter optimization, and advanced training strategies on model robustness and predictive accuracy.
The study emphasizes the significance of loss function optimization and threshold adjustment strategies in improving the detection of minority classes. The comparative results reveal distinct performance trade-offs across model architectures, with transformer models achieving superior contextual understanding at the cost of computational complexity. At the same time, deep learning approaches(LSTM models) offer efficiency advantages. These findings establish evidence-based guidelines for model selection in real-world content moderation systems, striking a balance between accuracy requirements and operational constraints.
Past Defense Notices
Anissa Khan
Privacy Preserving Biometric MatchingWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Perry Alexander, ChairPrasad Kulkarni
Fengjun Li
Abstract
Biometric matching is a process by which distinct features are used to identify an individual. Doing so privately is important because biometric data, such as fingerprints or facial features, is not something that can be easily changed or updated if put at risk. In this study, we perform a piece of the biometric matching process in a privacy preserving manner by using secure multiparty computation (SMPC). Using SMPC allows the identifying biological data, called a template, to remain stored by the data owner during the matching process. This provides security guarantees to the biological data while it is in use and therefore reduces the chances the data is stolen. In this study, we find that performing biometric matching using SMPC is just as accurate as performing the same match in plaintext.
Bryan Richlinski
Prioritize Program Diversity: Enumerative Synthesis with Entropy OrderingWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Sankha Guria, ChairPerry Alexander
Drew Davidson
Jennifer Lohoefener
Abstract
Program synthesis is a popular way to create a correct-by-construction program from a user-provided specification.
Term enumeration is a leading technique to systematically explore the space of programs by generating terms from a formal grammar.
These terms are treated as candidate programs which are tested/verified against the specification for correctness.
In order to prioritize candidates more likely to satisfy the specification, enumeration is often ordered by program size or other domain-specific heuristics.
However, domain-specific heuristics require expert knowledge, and enumeration by size often leads to terms comprised of frequently
repeating symbols that are less likely to satisfy a specification.
In this thesis, we build a heuristic that prioritizes term enumeration based on variability of individual symbols in the program, i.e.,
information entropy of the program. We use this heuristic to order programs in both top-down and bottom-up enumeration.
We evaluated our work on a subset of the PBE-String track of the 2017 SyGuS competition benchmarks and compared against size-based enumeration.
Top-down enumeration guided by entropy expands upon fewer partial expressions than naive in 77\% of benchmarks,
and tests fewer complete expressions in 54\%, resulting in improved synthesis time in 40\% of benchmarks.
However, 71\% of benchmarks in bottom-up enumeration using entropy tests fewer expressions than naive enumeration, without any improvements to the running time.
We conclude entropy is a promising direction to prioritize candidates during program search in enumerative synthesis,
and propose a future directions for improving performance of our proposed techniques.
Elizabeth Wyss
A New Frontier for Software Security: Diving Deep into npmWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Drew Davidson, ChairAlex Bardas
Fengjun Li
Bo Luo
J. Walker
Abstract
Open-source package managers (e.g., npm for Node.js) have become an established component of modern software development. Rather than creating applications from scratch, developers may employ modular software dependencies and frameworks--called packages--to serve as building blocks for writing larger applications. Package managers make this process easy. With a simple command line directive, developers are able to quickly fetch and install packages across vast open-source repositories. npm--the largest of such repositories--alone hosts millions of unique packages and serves billions of package downloads each week.
However, the widespread code sharing resulting from open-source package managers also presents novel security implications. Vulnerable or malicious code hiding deep within package dependency trees can be leveraged downstream to attack both software developers and the users of their applications. This downstream flow of software dependencies--dubbed the software supply chain--is critical to secure.
This research provides a deep dive into the npm-centric software supply chain, exploring various facets and phenomena that impact the security of this software supply chain. Such factors include (i) hidden code clones--which obscure provenance and can stealthily propagate known vulnerabilities, (ii) install-time attacks enabled by unmediated installation scripts, (iii) hard-coded URLs residing in package code, (iv) the impacts open-source development practices, and (v) package compromise via malicious updates. For each facet, tooling is presented to identify and/or mitigate potential security impacts. Ultimately, it is our hope that this research fosters greater awareness, deeper understanding, and further efforts to forge a new frontier for the security of modern software supply chains.
Jagadeesh Sai Dokku
Intelligent Chat Bot for KU Website: Automated Query Response and Resource NavigationWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
David Johnson, ChairPrasad Kulkarni
Hongyang Sun
Abstract
This project introduces an intelligent chatbot designed to improve user experience on our university website by providing instant, automated responses to common inquiries. Navigating a university website can be challenging for students, applicants, and visitors who seek quick information about admissions, campus services, events, and more. To address this challenge, we developed a chatbot that simulates human conversation using Natural Language Processing (NLP), allowing users to find information more efficiently. The chatbot is powered by a Bidirectional Long Short-Term Memory (BiLSTM) model, an architecture well-suited for understanding complex sentence structures. This model captures contextual information from both directions in a sentence, enabling it to identify user intent with high accuracy. We trained the chatbot on a dataset of intent-labeled queries, enabling it to recognize specific intentions such as asking about campus facilities, academic programs, or event schedules. The NLP pipeline includes steps like tokenization, lemmatization, and vectorization. Tokenization and lemmatization prepare the text by breaking it into manageable units and standardizing word forms, making it easier for the model to recognize similar word patterns. The vectorization process then translates this processed text into numerical data that the model can interpret. Flask is used to manage the backend, allowing seamless communication between the user interface and the BiLSTM model. When a user submits a query, Flask routes the input to the model, processes the prediction, and delivers the appropriate response back to the user interface. This chatbot demonstrates a successful application of NLP in creating interactive, efficient, and user-friendly solutions. By automating responses, it reduces reliance on manual support and ensures users can access relevant information at any time. This project highlights how intelligent chatbots can transform the way users interact with university websites, offering a faster and more engaging experience.
Anahita Memar
Optimizing Protein Particle Classification: A Study on Smoothing Techniques and Model PerformanceWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Prasad Kulkarni, ChairHossein Saiedian
Prajna Dhar
Abstract
This thesis investigates the impact of smoothing techniques on enhancing classification accuracy in protein particle datasets, focusing on both binary and multi-class configurations across three datasets. By applying methods including Averaging-Based Smoothing, Moving Average, Exponential Smoothing, Savitzky-Golay, and Kalman Smoothing, we sought to improve performance in Random Forest, Decision Tree, and Neural Network models. Initial baseline accuracies revealed the complexity of multi-class separability, while clustering analyses provided valuable insights into class similarities and distinctions, guiding our interpretation of classification challenges.
These results indicate that Averaging-Based Smoothing and Moving Average techniques are particularly effective in enhancing classification accuracy, especially in configurations with marked differences in surfactant conditions. Feature importance analysis identified critical metrics, such as IntMean and IntMax, which played a significant role in distinguishing classes. Cross-validation validated the robustness of our models, with Random Forest and Neural Network consistently outperforming others in binary tasks and showing promising adaptability in multi-class classification. This study not only highlights the efficacy of smoothing techniques for improving classification in protein particle analysis but also offers a foundational approach for future research in biopharmaceutical data processing and analysis.
Yousif Dafalla
Web-Armour: Mitigating Reconnaissance and Vulnerability Scanning with Injecting Scan-Impeding Delays in Web DeploymentsWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Alex Bardas, ChairDrew Davidson
Fengjun Li
Bo Luo
ZJ Wang
Abstract
Scanning hosts on the internet for vulnerable devices and services is a key step in numerous cyberattacks. Previous work has shown that scanning is a widespread phenomenon on the internet and commonly targets web application/server deployments. Given that automated scanning is a crucial step in many cyberattacks, it would be beneficial to make it more difficult for adversaries to perform such activity.
In this work, we propose Web-Armour, a mitigation approach to adversarial reconnaissance and vulnerability scanning of web deployments. The proposed approach relies on injecting scanning impeding delays to infrequently or rarely used portions of a web deployment. Web-Armour has two goals: First, increase the cost for attackers to perform automated reconnaissance and vulnerability scanning; Second, introduce minimal to negligible performance overhead to benign users of the deployment. We evaluate Web-Armour on live environments, operated by real users, and on different controlled (offline) scenarios. We show that Web-Armour can effectively lead to thwarting reconnaissance and internet-wide scanning.
Kabir Panahi
A Security Analysis of the Integration of Biometric Technology in the 2019 Afghan Presidential ElectionWhen & Where:
Nichols Hall, Room 250 (Gemini Room)
Committee Members:
Alex Bardas, ChairDrew Davidson
Fengjun Li
Bo Luo
Abstract
Afghanistan deployed Biometric Voter Verification (BVV) technology nationally for the first time in the 2019 presidential election to address the systematic frauds in the prior elections. Through semi-structure interviews with 18 key national and international stakeholders who had an active role in this election, this study investigates the gap between intended outcomes of the BVV technology—focused on voter enfranchisement, fraud prevention, and public trust—and the reality on election day and beyond within the unique socio-political and technical landscape of Afghanistan.
Our findings reveal that while BVV technology initially promised a secure and transparent election, various technical and implementation challenges emerged, including threats for voters, staff, and officials. We found that the BVVs both supported and violated electoral goals: while they helped reduce fraud, they inadvertently disenfranchised some voters and caused delays that affected public trust. Technical limitations, usability issues, and administrative misalignments contributed to these outcomes. This study recommends critical lessons for future implementations of electoral technologies, emphasizing the importance of context-aware technological solutions and the need for robust administrative and technical frameworks to fully realize the potential benefits of election technology in fragile democracies.
Hara Madhav Talasila
Radiometric Calibration of Radar Depth Sounder Data ProductsWhen & Where:
Nichols Hall, Room 317 (Richard K. Moore Conference Room)
Committee Members:
Carl Leuschen, ChairChristopher Allen
James Stiles
Jilu Li
Leigh Stearns
Abstract
Although the Center for Remote Sensing of Ice Sheets (CReSIS) performs several radar calibration steps to produce Operation IceBridge (OIB) radar depth sounder data products, these datasets are not radiometrically calibrated and the swath array processing uses ideal (rather than measured [calibrated]) steering vectors. Any errors in the steering vectors, which describe the response of the radar as a function of arrival angle, will lead to errors in positioning and backscatter that subsequently affect estimates of basal conditions, ice thickness, and radar attenuation. Scientific applications that estimate physical characteristics of surface and subsurface targets from the backscatter are limited with the current data because it is not absolutely calibrated. Moreover, changes in instrument hardware and processing methods for OIB over the last decade affect the quality of inter-seasonal comparisons. Recent methods which interpret basal conditions and calculate radar attenuation using CReSIS OIB 2D radar depth sounder echograms are forced to use relative scattering power, rather than absolute methods.
As an active target calibration is not possible for past field seasons, a method that uses natural targets will be developed. Unsaturated natural target returns from smooth sea-ice leads or lakes are imaged in many datasets and have known scattering responses. The proposed method forms a system of linear equations with the recorded scattering signatures from these known targets, scattering signatures from crossing flight paths, and the radiometric correction terms. A least squares solution to optimize the radiometric correction terms is calculated, which minimizes the error function representing the mismatch in expected and measured scattering. The new correction terms will be used to correct the remaining mission data. The radar depth sounder data from all OIB campaigns can be reprocessed to produce absolutely calibrated echograms for the Arctic and Antarctic. A software simulator will be developed to study calibration errors and verify the calibration software. The software for processing natural targets and crossovers will be made available in CReSIS’s open-source polar radar software toolbox. The OIB data will be reprocessed with new calibration terms, providing to the data user community a complete set of radiometrically calibrated radar echograms for the CReSIS OIB radar depth sounder for the first time.
Daniel Herr
Information Theoretic Waveform Design with Application to Physically Realizable Adaptive-on-Transmit RadarWhen & Where:
Nichols Hall, Room 129 (Ron Evans Apollo Auditorium)
Committee Members:
James Stiles, ChairChristopher Allen
Carl Leuschen
Chris Depcik
Abstract
The fundamental task of a radar system is to utilize the electromagnetic spectrum to sense a scattering environment and generate some estimate from this measurement. This task can be posed as a Bayesian estimation problem of random parameters (the scattering environment) through an imperfect sensor (the radar system). From this viewpoint, metrics such as error covariance and estimator precision (or information) can be leveraged to evaluate and improve the performance of radar systems. Here, physically realizable radar waveforms are designed to maximize the Fisher information (FI) (specifically, a derivative of FI known as marginal Fisher information (MFI)) extracted from a scattering environment thereby minimizing the expected error covariance about an estimation parameter space. This information theoretic framework, along with the high-degree of design flexibility afforded by fully digital transmitter and receiver architectures, creates a high-dimensionality design space for optimizing radar performance.
First, the problem of joint-domain range-Doppler estimation utilizing a pulse-agile radar is posed from an estimation theoretic framework, and the minimum mean square error (MMSE) estimator is shown to suppress the range-sidelobe modulation (RSM) induced by pulse agility which may improve the signal-to-interference-plus-noise ratio (SINR) in signal-limited scenarios. A computationally efficient implementation of the range-Doppler MMSE estimator is developed as a series of range-profile estimation problems, under specific modeling and statistical assumptions. Next, a transformation of the estimation parameterization is introduced which ameliorates the high noise-gain typically associated with traditional MMSE estimation by sacrificing the super-resolution achieved by the MMSE estimator. Then, coordinate descent and gradient descent optimization methods are developed for designing MFI optimal waveforms with respect to either the original or transformed estimation space. These MFI optimal waveforms are extended to provide pulse-agility, which produces high-dimensionality radar emissions amenable to non-traditional receive processing techniques (such as MMSE estimation). Finally, informationally optimal waveform design and optimal estimation are extended into a cognitive radar concept capable of adaptive and dynamic sensing. The efficacy of the MFI waveform design and MMSE estimation are demonstrated via open-air hardware experimentation where their performance is compared against traditional techniques
Matthew Heintzelman
Spatially Diverse Radar Techniques - Emission Optimization and Enhanced Receive ProcessingWhen & Where:
Nichols Hall, Room 129 (Ron Evans Apollo Auditorium)
Committee Members:
Shannon Blunt, ChairChristopher Allen
Patrick McCormick
James Stiles
Zsolt Talata
Abstract
Radar systems perform 3 basic tasks: search/detection, tracking, and imaging. Traditionally, varied operational and hardware requirements have compartmentalized these functions to distinct and specialized radars, which may communicate actionable information between them. Expedited by the growth in computational capabilities modeled by Moore’s law, next-generation radars will be sophisticated, multi-function systems comprising generalized and reprogrammable subsystems. The advance of fully Digital Array Radars (DAR) has enabled the implementation of highly directive phased arrays that can scan, detect, and track scatterers through a volume-of-interest. Conversely, DAR technology has also enabled Multiple-Input Multiple-Output (MIMO) radar methodologies that seek to illuminate all space on transmit, while forming separate but simultaneous, directive beams on receive.
Waveform diversity has been repeatedly proven to enhance radar operation through added Degrees-of-Freedom (DoF) that can be leveraged to expand dynamic range, provide ambiguity resolution, and improve parameter estimation. In particular, diversity among the DAR’s transmitting elements provides flexibility to the emission, allowing simultaneous multi-function capability. By precise design of the emission, the DAR can utilize the operationally-continuous trade-space between a fully coherent phased array and a fully incoherent MIMO system. This flexibility could enable the optimal management of the radar’s resources, where Signal-to-Noise Ratio (SNR) would be traded for robustness in detection, measurement capability, and tracking.
Waveform diversity is herein leveraged as the predominant enabling technology for multi-function radar emission design. Three methods of emission optimization are considered to design distinct beams in space and frequency, according to classical error minimization techniques. First, a gradient-based optimization of the Space-Frequency Template Error (SFTE) is applied to a high-fidelity model for a wideband array’s far-field emission. Second, a more efficient optimization is considered, based on the SFTE for narrowband arrays. Finally, a suboptimal solution, based on alternating projections, is shown to provide rapidly reconfigurable transmit patterns. To improve the dynamic range observed for MIMO radars employing pulse-agile quasi-orthogonal waveforms, a pulse-compression model is derived that manages to suppress both autocorrelation sidelobes and multi-transmitter-induced cross-correlation. The proposed waveforms and filters are implemented in hardware to demonstrate performance, validate robustness, and reflect real-world application to the degree possible with laboratory experimentation.