Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

Transceiver design and system optimization for ultra-wideband communications

This dissertation investigates the potential promises and proposes possible solutions to the challenges of designing transceivers and optimizing system parameters in ultra-wideband (UWB) systems. The goal is to provide guidelines for UWB transceiver implementations under constraints by regulation, existing interference, and channel estimation. New UWB pulse shapes are invented that satisfy the Federal Communications Commission spectral mask. Parameters are designed to possibly implement the proposed pulses. A link budget is quantified based on an accurate frequency-dependent path loss calculation to account for variations across the ultra-wide bandwidth of the signal. Achievable information rates are quantified as a function of transmission distance over additive white Gaussian noise and multipath channels under specific UWB constraints: limited power spectral density, specific modulation formats, and a highly dispersive channel. The effect of self-interference (SI) and inter-symbol interference (ISI) on channel capacity is determined, and modulation formats that mitigate against this effect is identified. Spreading gains of familiar UWB signaling formats are evaluated, and UWB signals are proved to be spread spectrum. Conditions are formulated for trading coding gain with spreading gain with only a small impact on performance. Numerical results are examined to demonstrate that over a frequency-selective channel, the spreading gain may be beneficial in reducing the SI and ISI resulting in higher information rates. A reduced-rank adaptive filtering technique is applied to the problem of interference suppression and optimum combining in UWB communications. The reduced-rank combining method, in particular the eigencanceler, is proposed and compared with a minimum mean square error Rake receiver. Simulation results are evaluated to show that the performance of the proposed method is superior to the minimum mean square error when the correlation matrix is estimated from limited data. Impact of channel estimation on UWB system performance is investigated when path delays and path amplitudes are jointly estimated. Cramér-Rao bound (CRB) expressions for the variance of path delay and amplitude estimates are formulated using maximum likelihood estimation. Using the errors obtained from the CRB, the effective signal-to-noise ratio for UWB Rake receivers employing maximum ratio combining (MRC) is devised in the presence of channel path delay and amplitude errors. An exact expression of the bit error rate (BER) for UWB Rake receivers with MRC is derived with imperfect estimates of channel path delays and amplitudes. Further, this analysis is applied to design optimal transceiver parameters. The BER is used as part of a binary symmetric channel and the achievable information rates are evaluated. The optimum power allocation and number of symbols allocated to the pilot are developed with respect to maximizing the information rate. The optimal signal bandwidth to be used for UWB communications is determined in the presence of imperfect channel state information. The number of multipath components to be collected by Rake receivers is designed to optimize performance with non-ideal channel estimation

Ultra Wideband Communications: from Analog to Digital

Ultrabreitband-Signale (Ultra Wideband [UWB]) können einen signifikanten Nutzen im Bereich drahtloser Kommunikationssysteme haben. Es sind jedoch noch einige Probleme offen, die durch Systemdesigner und Wissenschaftler gelöst werden müssen. Ein Funknetzsystem mit einer derart großen Bandbreite ist normalerweise auch durch eine große Anzahl an Mehrwegekomponenten mit jeweils verschiedenen Pfadamplituden gekennzeichnet. Daher ist es schwierig, die zeitlich verteilte Energie effektiv zu erfassen. Außerdem ist in vielen Fällen der naheliegende Ansatz, ein kohärenter Empfänger im Sinne eines signalangepassten Filters oder eines Korrelators, nicht unbedingt die beste Wahl. In der vorliegenden Arbeit wird dabei auf die bestehende Problematik und weitere Lösungsmöglichkeiten eingegangen. Im ersten Abschnitt geht es um „Impulse Radio UWB”-Systeme mit niedriger Datenrate. Bei diesen Systemen kommt ein inkohärenter Empfänger zum Einsatz. Inkohärente Signaldetektion stellt insofern einen vielversprechenden Ansatz dar, als das damit aufwandsgünstige und robuste Implementierungen möglich sind. Dies trifft vor allem in Anwendungsfällen wie den von drahtlosen Sensornetzen zu, wo preiswerte Geräte mit langer Batterielaufzeit nötigsind. Dies verringert den für die Kanalschätzung und die Synchronisation nötigen Aufwand, was jedoch auf Kosten der Leistungseffizienz geht und eine erhöhte Störempfindlichkeit gegenüber Interferenz (z.B. Interferenz durch mehrere Nutzer oder schmalbandige Interferenz) zur Folge hat. Um die Bitfehlerrate der oben genannten Verfahren zu bestimmen, wurde zunächst ein inkohärenter Combining-Verlust spezifiziert, welcher auftritt im Gegensatz zu kohärenter Detektion mit Maximum Ratio Multipath Combining. Dieser Verlust hängt von dem Produkt aus der Länge des Integrationsfensters und der Signalbandbreite ab. Um den Verlust durch inkohärentes Combining zu reduzieren und somit die Leistungseffizienz des Empfängers zu steigern, werden verbesserte Combining-Methoden für Mehrwegeempfang vorgeschlagen. Ein analoger Empfänger, bei dem der Hauptteil des Mehrwege-Combinings durch einen „Integrate and Dump”-Filter implementiert ist, wird für UWB-Systeme mit Zeit-Hopping gezeigt. Dabei wurde die Einsatzmöglichkeit von dünn besetzten Codes in solchen System diskutiert und bewertet. Des Weiteren wird eine Regel für die Code-Auswahl vorgestellt, welche die Stabilität des Systems gegen Mehrnutzer-Störungen sicherstellt und gleichzeitig den Verlust durch inkohärentes Combining verringert. Danach liegt der Fokus auf digitalen Lösungen bei inkohärenter Demodulation. Im Vergleich zum Analogempfänger besitzt ein Digitalempfänger einen Analog-Digital-Wandler im Zeitbereich gefolgt von einem digitalen Optimalfilter. Der digitale Optimalfilter dekodiert den Mehrfachzugriffscode kohärent und beschränkt das inkohärente Combining auf die empfangenen Mehrwegekomponenten im Digitalbereich. Es kommt ein schneller Analog-Digital-Wandler mit geringer Auflösung zum Einsatz, um einen vertretbaren Energieverbrauch zu gewährleisten. Diese Digitaltechnik macht den Einsatz langer Analogverzögerungen bei differentieller Demodulation unnötig und ermöglicht viele Arten der digitalen Signalverarbeitung. Im Vergleich zur Analogtechnik reduziert sie nicht nur den inkohärenten Combining-Verlust, sonder zeigt auch eine stärkere Resistenz gegenüber Störungen. Dabei werden die Auswirkungen der Auflösung und der Abtastrate der Analog-Digital-Umsetzung analysiert. Die Resultate zeigen, dass die verminderte Effizienz solcher Analog-Digital-Wandler gering ausfällt. Weiterhin zeigt sich, dass im Falle starker Mehrnutzerinterferenz sogar eine Verbesserung der Ergebnisse zu beobachten ist. Die vorgeschlagenen Design-Regeln spezifizieren die Anwendung der Analog-Digital-Wandler und die Auswahl der Systemparameter in Abhängigkeit der verwendeten Mehrfachzugriffscodes und der Modulationsart. Wir zeigen, wie unter Anwendung erweiterter Modulationsverfahren die Leistungseffizienz verbessert werden kann und schlagen ein Verfahren zur Unterdrückung schmalbandiger Störer vor, welches auf Soft Limiting aufbaut. Durch die Untersuchungen und Ergebnissen zeigt sich, dass inkohärente Empfänger in UWB-Kommunikationssystemen mit niedriger Datenrate ein großes Potential aufweisen. Außerdem wird die Auswahl der benutzbaren Bandbreite untersucht, um einen Kompromiss zwischen inkohärentem Combining-Verlust und Stabilität gegenüber langsamen Schwund zu erreichen. Dadurch wurde ein neues Konzept für UWB-Systeme erarbeitet: wahlweise kohärente oder inkohärente Empfänger, welche als UWB-Systeme Frequenz-Hopping nutzen. Der wesentliche Vorteil hiervon liegt darin, dass die Bandbreite im Basisband sich deutlich verringert. Mithin ermöglicht dies einfach zu realisierende digitale Signalverarbeitungstechnik mit kostengünstigen Analog-Digital-Wandlern. Dies stellt eine neue Epoche in der Forschung im Bereich drahtloser Sensorfunknetze dar. Der Schwerpunkt des zweiten Abschnitts stellt adaptiven Signalverarbeitung für hohe Datenraten mit „Direct Sequence”-UWB-Systemen in den Vordergrund. In solchen Systemen entstehen, wegen der großen Anzahl der empfangenen Mehrwegekomponenten, starke Inter- bzw. Intrasymbolinterferenzen. Außerdem kann die Funktionalität des Systems durch Mehrnutzerinterferenz und Schmalbandstörungen deutlich beeinflusst werden. Um sie zu eliminieren, wird die „Widely Linear”-Rangreduzierung benutzt. Dabei verbessert die Rangreduzierungsmethode das Konvergenzverhalten, besonders wenn der gegebene Vektor eine sehr große Anzahl an Abtastwerten beinhaltet (in Folge hoher einer Abtastrate). Zusätzlich kann das System durch die Anwendung der R-linearen Verarbeitung die Statistik zweiter Ordnung des nicht-zirkularen Signals vollständig ausnutzen, was sich in verbesserten Schätzergebnissen widerspiegelt. Allgemeine kann die Methode der „Widely Linear”-Rangreduzierung auch in andern Bereichen angewendet werden, z.B. in „Direct Sequence”-Codemultiplexverfahren (DS-CDMA), im MIMO-Bereich, im Global System for Mobile Communications (GSM) und beim Beamforming.The aim of this thesis is to investigate key issues encountered in the design of transmission schemes and receiving techniques for Ultra Wideband (UWB) communication systems. Based on different data rate applications, this work is divided into two parts, where energy efficient and robust physical layer solutions are proposed, respectively. Due to a huge bandwidth of UWB signals, a considerable amount of multipath arrivals with various path gains is resolvable at the receiver. For low data rate impulse radio UWB systems, suboptimal non-coherent detection is a simple way to effectively capture the multipath energy. Feasible techniques that increase the power efficiency and the interference robustness of non-coherent detection need to be investigated. For high data rate direct sequence UWB systems, a large number of multipath arrivals results in severe inter-/intra-symbol interference. Additionally, the system performance may also be deteriorated by multi-user interference and narrowband interference. It is necessary to develop advanced signal processing techniques at the receiver to suppress these interferences. Part I of this thesis deals with the co-design of signaling schemes and receiver architectures in low data rate impulse radio UWB systems based on non-coherent detection.● We analyze the bit error rate performance of non-coherent detection and characterize a non-coherent combining loss, i.e., a performance penalty with respect to coherent detection with maximum ratio multipath combining. The thorough analysis of this loss is very helpful for the design of transmission schemes and receive techniques innon-coherent UWB communication systems.● We propose to use optical orthogonal codes in a time hopping impulse radio UWB system based on an analog non-coherent receiver. The “analog” means that the major part of the multipath combining is implemented by an integrate and dump filter. The introduced semi-analytical method can help us to easily select the time hopping codes to ensure the robustness against the multi-user interference and meanwhile to alleviate the non-coherent combining loss.● The main contribution of Part I is the proposal of applying fully digital solutions in non-coherent detection. The proposed digital non-coherent receiver is based on a time domain analog-to-digital converter, which has a high speed but a very low resolution to maintain a reasonable power consumption. Compared to its analog counterpart, itnot only significantly reduces the non-coherent combining loss but also offers a higher interference robustness. In particular, the one-bit receiver can effectively suppress strong multi-user interference and is thus advantageous in separating simultaneously operating piconets.The fully digital solutions overcome the difficulty of implementing long analog delay lines and make differential UWB detection possible. They also facilitate the development of various digital signal processing techniques such as multi-user detection and non-coherent multipath combining methods as well as the use of advanced modulationschemes (e.g., M-ary Walsh modulation).● Furthermore, we present a novel impulse radio UWB system based on frequency hopping, where both coherent and non-coherent receivers can be adopted. The key advantage is that the baseband bandwidth can be considerably reduced (e.g., lower than 500 MHz), which enables low-complexity implementation of the fully digital solutions. It opens up various research activities in the application field of wireless sensor networks. Part II of this thesis proposes adaptive widely linear reduced-rank techniques to suppress interferences for high data rate direct sequence UWB systems, where second-order non-circular signals are used. The reduced-rank techniques are designed to improve the convergence performance and the interference robustness especially when the received vector contains a large number of samples (due to a high sampling rate in UWB systems). The widely linear processing takes full advantage of the second-order statistics of the non-circular signals and enhances the estimation performance. The generic widely linear reduced-rank concept also has a great potential in the applications of other systems such as Direct Sequence Code Division Multiple Access (DS-CDMA), Multiple Input Multiple Output (MIMO) system, and Global System for Mobile Communications (GSM), or in other areas such as beamforming

ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar

Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in super-resolution stepped frequency radar angle-range-doppler imaging. Tailored to an uncooperative scenario wherein a MIMO radar shares spectrum with communications, the ADMM-Net recovers the radar image---which is assumed to be sparse---and simultaneously removes the communication interference, which is modeled as sparse in the frequency domain owing to spectrum underutilization. The scenario motivates an $\ell_1$-minimization problem whose ADMM iteration, in turn, undergirds the neural network design, yielding a set of generalized ADMM iterations that have learnable hyperparameters and operations. To train the network we use random data generated according to the radar and communication signal models. In numerical experiments ADMM-Net exhibits markedly lower error and computational cost than ADMM and CVX

Machine Learning for Beamforming in Audio, Ultrasound, and Radar

Multi-sensor signal processing plays a crucial role in the working of several everyday technologies, from correctly understanding speech on smart home devices to ensuring aircraft fly safely. A specific type of multi-sensor signal processing called beamforming forms a central part of this thesis. Beamforming works by combining the information from several spatially distributed sensors to directionally filter information, boosting the signal from a certain direction but suppressing others. The idea of beamforming is key to the domains of audio, ultrasound, and radar. Machine learning is the other central part of this thesis. Machine learning, and especially its sub-field of deep learning, has enabled breakneck progress in tackling several problems that were previously thought intractable. Today, machine learning powers many of the cutting edge systems we see on the internet for image classification, speech recognition, language translation, and more. In this dissertation, we look at beamforming pipelines in audio, ultrasound, and radar from a machine learning lens and endeavor to improve different parts of the pipelines using ideas from machine learning. We start off in the audio domain and derive a machine learning inspired beamformer to tackle the problem of ensuring the audio captured by a camera matches its visual content, a problem we term audiovisual zooming. Staying in the audio domain, we then demonstrate how deep learning can be used to improve the perceptual qualities of speech by denoising speech clipping, codec distortions, and gaps in speech. Transitioning to the ultrasound domain, we improve the performance of short-lag spatial coherence ultrasound imaging by exploiting the differences in tissue texture at each short lag value by applying robust principal component analysis. Next, we use deep learning as an alternative to beamforming in ultrasound and improve the information extraction pipeline by simultaneously generating both a segmentation map and B-mode image of high quality directly from raw received ultrasound data. Finally, we move to the radar domain and study how deep learning can be used to improve signal quality in ultra-wideband synthetic aperture radar by suppressing radio frequency interference, random spectral gaps, and contiguous block spectral gaps. By training and applying the networks on raw single-aperture data prior to beamforming, it can work with myriad sensor geometries and different beamforming equations, a crucial requirement in synthetic aperture radar

Air Force Institute of Technology Research Report 2004

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

Vital Signs Monitoring Based On UWB Radar

Contactless detection of human vital sign using radar sensors appears to be a promising technology which integrates communication, biomedicine, computer science etc. The radar-based vital sign detection has been actively investigated in the past decade. Due to the advantages such as wide bandwidth, high resolution, small and portable size etc., ultra-wideband (UWB) radar has received a great deal of attention in the health care field. In this thesis, an X4 series UWB radar developed by Xethru Company is adopted to detect human breathing signals through the radar echo reflected by the chest wall movement caused by breath and heartbeat. The emphasis is placed on the estimation of breathing and heart rate based on several signal processing algorithms. Firstly, the research trend of vital sign detection using radar technology is reviewed, based on which the advantages of contactless detection and UWB radar-based technology are highlighted. Then theoretical basis and core algorithms of radar signals detection are presented. Meanwhile, the detection system based on Xethru UWB radar is introduced. Next, several preprocessing methods including SVD-based clutter and noise removal algorithms, the largest variance-based target detection method, and the autocorrelation-based breathing-like signal identification method are investigated, to extract the significant component containing the vital signs from the received raw radar echo signal. Then the thesis investigates four time-frequency analysis algorithms (fast Fourier transform + band-pass filter (FFT+BPF), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) and compare their performances in estimating breathing rate (BR) and heart rate (HR) in different application scenarios. A python-based vital signs detection system is designed to implement the above-mentioned preprocessing and BR and HR estimation algorithms, based on which a large number of single target experiments are undertaken to evaluate the four estimation algorithms. Specifically, the single target experiments are divided into simple setup and challenging setup. In the simple setup, testees face to radar and keep normal breathing in an almost stationary posture, while in the challenging setup, the testee is allowed to do more actions, such as site sitting, changing the breathing frequency, deep hold the breathing. It is shown that the FFT+BPF algorithm gives the highest accuracy and the fastest calculation speed under the simple setup, while in a challenging setup, the VMD algorithm has the highest accuracy and the widest applicability. Finally, double targets breathing signal detection at different distances to the radar are undertaken, aiming to observe whether the breathing signals of two targets will interfere with each other. We found that when two objects are not located at the same distance to the radar, the object closer to the radar will not affect the breath detection of the object far from the radar. When the two targets are located at the same distance, the 'Shading effect' appears in the two breathing signals and only VMD algorithm can separate the breathing signals of the targets

Realistic chipless RFID: identification and localization

Für die weitere Massenverbreitung von RFID Systemen ist ein günstiges und genaues Verfahren zur Objektlokalisierung und –verfolgung zwingend erforderlich. Chiplose RFID Systeme erlauben im Gegensatz zu herkömmlichen chipbehafteten RFID Systemen den Einsatz von einfachen, druckbaren RFID Tags, eine Möglichkeit zum Einstieg in die Ära von extrem billigen RFID Tags. Diese Dissertation konzentriert sich auf die Lösung von drei Herausforderungen bei der Erkennung von chiplosen RFID Tags innerhalb geschlossener Räume. Der erste in der vorliegenden Arbeit diskutierte Aspekt beschäftigt sich mit Methoden zum Eliminieren des Störechos der Umgebung (clutter removal techniques). Im chiplosen RFID System ist das Umgebungsstörecho definiert durch das von der Umgebung reflektierte Signal, das nicht mit dem RFID Tag interagiert. Die Stärke dieses Signals ist in jedem Fall größer als die des vom RFID Tag zurückgestrahlten (backscattered) Signals, was die Signaturerkennung des RFID Tags unmöglich macht. Zur Lösung dieses Problems schlage ich zwei Algorithmen vor. Der erste ist die Leerraum-Kalibrierung (empty room calibration). Bei diesem Algorithmus werden die Messungen mit RFID Tag von denen ohne RFID Tags abgezogen. Der zweite Algorithmus basiert auf dem Rake-Receiver unter Nutzung einer Zufallsfolge (PN sequence), er erfordert keine zusätzliche Kalibrierung. Der zweite Aspekt betrifft die Notch Erkennung und Identifikation, ein sehr wichtiger Bereich des chiplosen RFID Systems. Er ist dafür verantwortlich, die Notchs in Bits umzuwandeln. Für eine effektive Detektion werden Windowing (Fenster) Verfahren vorgeschlagen, wobei jedes Fenster einen oder auch keinen Notch beinhalten kann. Insgesamt drei neue Verfahren zur Notch Erkennung wurden implementiert. Als erstes ein Matched Filter (MF), in dem der einkommende Notch mit einem Referenz Notch verglichen wird. Das zweite Verfahren basiert auf einer gefensterten Singulärwertzerlegung, damit kann sowohl der Notch erkannt werden, als auch seine Bandbreite bestimmt werden. Als drittes Verfahren wird das dynamische Frequency Warping vorgestellt. Diese Technik nutzt nichtlineare um die Notche unddie Frequenzverschiebungen, die an den Notches auftreten, zu erkennen. Als dritter Aspekt wird die Lokalisierung der RFID Tags in dieser Dissertation diskutiert. Dazu werden zwei Algorithmen erklärt und implementiert. Der erste Algorithmus beruht auf der Triangulation durch drei getrennte RFID Lesegeräte, während sich der zweite die Position des RFID Tags aus der Signalstärke und dem Winkel des vom RFID Tag kommenden Signals berechnet. Alle genannten Algorithmen und Verfahren wurden in einer realen Innenraum Testumgebung mit RFID Tags und einer Software Defined Radio (SDR) Plattform vermessen, um die Zuverlässigkeit der Algorithmen unter normalen Bedingungen zu überprüfen.For mass deployment of RFID systems, cheap and accurate item level identification and tracking are profoundly needed. Fortunately, unlike conventional chip-based RFID, chipless RFID systems offers low-cost printable tags holding a better chance to enter the era of penny-cost tags. This dissertation concentrated on solving three challenges in the detection of the chipless tag inside an indoor environment. The first aspect discussed in the thesis are the chipless RFID clutter removal techniques. In chipless RFID the environmental clutter response is defined as the signal reflected from the environment, that does not interact with the tag. This signal has higher power than the backscattered signal from the tag, rendering the tag signature undetectable. Two algorithms to overcome this problem was used, the first is empty room calibration. The first algorithm is based on subtracting the measurement with the tag from the one without. The second algorithm is Rake receiver using PN sequence; this algorithm requires no pre-measurement calibration. The second aspect is notch detection and identification which is a critical part of the chipless system. This part is responsible for converting the notches into bits. For effective detection, a windowing operation is proposed, where each window may contain a notch or not. Three novel techniques are implemented to detect the notch. The first is matched filter were a reference notch is compared with the incoming signal. The second is window based singular value decomposition, where a constellation is created to detect not only the existence of a notch but also the bandwidth of the notch. The third notch detection technique is dynamic frequency warping. This technique utilizes non-linear warping to detect the notch and the frequency shifts that occurs on the notch. The third aspect discussed in the thesis is tag localization. In this aspect, two algorithms are implemented and explained. The first is trilateration which requires three different readers. The second localization algorithm exploits received signal strength and angle of arrival to detect the location of the tag accurately. All the algorithms were tested using a real testbed to validate the reliability of the techniques. The measurements were done using fabricated tags in an indoor environment using Software Defines Radio (SDR)

Adaptive waveform design for SAR in a crowded spectrum

This thesis concerns the development of an adaptive waveform design scheme for synthetic aperture radar (SAR) to support its operation in the increasingly crowded radio frequency (RF) spectrum, focusing on mitigating the effects of external RF interference. The RF spectrum is a finite resource and the rapid expansion of the telecommunications industry has seen radar users face a significant restriction in the range of available operational frequencies. This crowded spectrum scenario leads to increased likelihood of RF interference either due to energy leakage from neighbouring spectral users or from unlicensed transmitters. SAR is a wide bandwidth radar imaging mode which exploits the motion of the radar platform to form an image using multiple one dimensional profiles of the scene of interest known as the range profile. Due to its wideband nature, SAR is particularly vulnerable to RF interference which causes image impairments and overall reduction in quality. Altering the approach for radar energy transmission across the RF spectrum is now imperative to continue effective operation. Adaptive waveforms have recently become feasible for implementation and offer the much needed flexibility in the choice and control over radar transmission. However, there is a critically small processing time frame between waveform reception and transmission, which necessitates the use of computationally efficient processing algorithms to use adaptivity effectively. This simulation-based study provides a first look at adaptive waveform design for SAR to mitigate the detrimental effects of RF interference on a pulse-to-pulse basis. Standard SAR systems rely on a fixed waveform processing format on reception which restricts its potential to reap the benefits of adaptive waveform design. Firstly, to support waveform design for SAR, system identification techniques are applied to construct an alternative receive processing method which allows flexibility in waveform type. This leads to the main contribution of the thesis which is the formation of an adaptive spectral waveform design scheme. A computationally efficient closed-form expression for the waveform spectrum that minimizes the error in the estimate of the SAR range profile on a pulse to pulse basis is derived. The range profile and the spectrum of the interference are estimated at each pulse. The interference estimate is then used to redesign the proceeding waveform for estimation of the range profile at the next radar platform position. The solution necessitates that the energy is spread across the spectrum such that it competes with the interferer. The scenario where the waveform admits gaps in the spectrum in order to mitigate the effects of the interference is also detailed and is the secondary major thesis contribution. A series of test SAR images demonstrate the efficacy of these techniques and yield reduced interference effects compared to the standard SAR waveform