Suppression approach to main-beam deceptive jamming in FDA-MIMO radar using nonhomogeneous sample detection

Suppressing the main-beam deceptive jamming in traditional radar systems is challenging. Furthermore, the observations corrupted by false targets generated by smart deceptive jammers, which are not independent and identically distributed because of the pseudo-random time delay. This in turn complicates the task of jamming suppression. In this paper, a new main-beam deceptive jamming suppression approach is proposed, using nonhomogeneous sample detection in the frequency diverse array-multiple-input and multiple-output radar with non-perfectly orthogonal waveforms. First, according to the time delay or range difference, the true and false targets are discriminated in the joint transmit-receive spatial frequency domain. Subsequently, due to the range mismatch, the false targets are suppressed through a transmit-receive 2-D matched filter. In particular, in order to obtain the jamming-plus-noise covariance matrix with high accuracy, a nonhomogeneous sample detection method is developed. Simulation results are provided to demonstrate the detection performance of the proposed approach

The Atmospheric Imaging Radar for High Resolution Observations of Severe Weather

Mobile weather radars often utilize rapid scan strategies when collecting obser- vations of severe weather. Various techniques have been used to improve volume update times, including the use of agile and multi-beam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field-of-view of the radar, which is defined by a broad transmit beam. As a result, imaging radars provide update rates significantly exceeding those of existing mobile radars, including phased arrays. The Atmospheric Radar Research Center at the University of Oklahoma is engaged in the design, construction and testing of a mobile imaging weather radar system called the Atmospheric Imaging Radar (AIR).Initial tests performed with the AIR demonstrate the benefits and versatility of utilizing beamforming techniques to achieve high spatial and temporal resolution. Specifically, point target analysis was performed using several digital beamform- ing techniques. Adaptive algorithms allow for the improved resolution and clutter rejection when compared to traditional techniques. Additional experiments were conducted during three severe weather events in Oklahoma, including an isolated cell event with high surface winds, a squall line, and a non-tornadic cyclone. Sev- eral digital beamforming techniques were tested and analyzed, producing unique, simultaneous multi-beam measurements using the AIR.The author made specific contributions to the field of radar meteorology in several areas. Overseeing the design and construction of the AIR was a signif- icant effort and involved the coordination of many smaller teams. Interacting with the members of each group and ensuring the success of the project was a primary focus throughout the venture. Meteorological imaging radars of the past have typically focused on boundary layer or upper atmospheric phenomena. The AIR's primary focus is to collect precipitation data from severe weather. Ap- plying well defined beamforming techniques, ranging from Fourier to adaptive algorithms like robust Capon and Amplitude and Phase Estimation (APES), to precipitation phenomena was a unique effort and has served to advance the use of adaptive array processing in radar meteorology. Exploration of irregular antenna spacing and drawing from the analogies between temporal and spatial process- ing led to the development of a technique that reduced the impact of grating lobes by unwrapping angular ambiguities. Ultimately, the author leaves having created a versatile platform capable of producing some of the highest resolution weather data available in the research community today, with opportunities to significantly advance the understanding of rapidly evolving weather phenomena and severe storms

Time-invariant joint transmit and receive beampattern optimization for polarization-subarray based frequency-diverse-array radar

We propose a polarization-subarray based frequency diverse array (FDA) radar with the subarray-based FDA as transmit (Tx) array and the polarization-sensitive subarray-based FDA (PSFDA) as the receive (Rx) array. The subarray-based FDA has the capability to achieve a single maximum beampattern at the target location, while the PSFDA can provide an extra degree of freedom to further suppress the interference and thus to improve the radar's signal-to-interference-plus-noise ratio (SINR). The time-dependent frequency offsets are designed for the proposed radar to realize the time-invariant beampattern at the desired target location over the whole pulse duration. To further improve the target detection performance, the time-invariant joint Tx-Rx beampattern design is considered based on the output SINR maximization. To effectively solve the nonconvex output SINR maximization problem, a suboptimal alternating optimization algorithm is proposed to iteratively optimize the FDA Tx beamforming, the PSFDA spatial pointings and the PSFDA Rx beamforming. Numerical experiments illustrate that the time-invariant and single-maximum joint Tx-Rx beampattern at the target location is achieved. Moreover, compared to the basic FDA and logarithmic frequency offset FDA as well as conventional phased array radars, the proposed polarization-subarray based FDA radar achieves a significant SINR improvement, particularly when the desired target and the interferences are spatially indistinguishable.</p

Analysis and design of smart antenna arrays (SAAs) for improved directivity at GHz range for wireless communication systems.

Doctor of Philosophy in Electronic Engineering. University of KwaZulu-Natal, Durban 2018.Abstract available in PDF file

Generating pictures from waves : aspects of image formation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 171-177).The research communities, technologies, and tools for image formation are diverse. On the one hand, computer vision and graphics researchers analyze incoherent light using coarse geometric approximations from optics. On the other hand, array signal processing and acoustics researchers analyze coherent sound waves using stochastic estimation theory and diffraction formulas from physics. The ability to inexpensively fabricate analog circuitry and digital logic for millimeter-wave radar and ultrasound creates opportunities in comparing diverse perspectives on image formation, and presents challenges in implementing imaging systems that scale in size. We present algorithms, architectures, and abstractions for image formation that relate the different communities, technologies, and tools. We address practical technical challenges in operating millimeter-wave radar and ultrasound systems in the presence of phase noise and scattering. We model a broad class of physical phenomena with isotropic point sources. We show that the optimal source location estimator for coherent waves reduces to processing an image produced by a conventional camera, provided the sources are well separated relative to the system resolution, and in the limit of small wavelength and globally incoherent light. We introduce quasi light fields to generalize the incoherent image formation process to coherent waves, offering resolution tradeoffs that surpass the traditional Fourier uncertainty principle by leveraging time-frequency distributions. We show that the number of sensors in a coherent imaging array defines a stable operating point relative to the phase noise. We introduce a digital phase tightening algorithm to reduce phase noise. We present a system identification framework for multiple-input multiple-output (MIMO) ultrasound imaging that generalizes existing approaches with time-varying filters. Our theoretical results enable the application of traditional techniques in incoherent imaging to coherent imaging, and vice versa. Our practical results suggest a methodology for designing millimeter-wave imaging systems. Our conclusions reinforce architectural principles governing transmitter and receiver design, the role of analog and digital circuity, and the tradeoff between data rate and data precision.by Anthony Accardi.Ph.D

Generating Pictures from Waves: Aspects of Image Formation

Thesis Supervisor: Gregory W. Wornell Title: Professor of Electrical Engineering and Computer ScienceThe research communities, technologies, and tools for image formation are diverse. On the one hand, computer vision and graphics researchers analyze incoherent light using coarse geometric approximations from optics. On the other hand, array signal processing and acoustics researchers analyze coherent sound waves using stochastic estimation theory and diffraction formulas from physics. The ability to inexpensively fabricate analog circuitry and digital logic for millimeter-wave radar and ultrasound creates opportunities in comparing diverse perspectives on image formation, and presents challenges in implementing imaging systems that scale in size. We present algorithms, architectures, and abstractions for image formation that relate the different communities, technologies, and tools. We address practical technical challenges in operating millimeter-wave radar and ultrasound systems in the presence of phase noise and scattering. We model a broad class of physical phenomena with isotropic point sources. We show that the optimal source location estimator for coherent waves reduces to processing an image produced by a conventional camera, provided the sources are wellseparated relative to the system resolution, and in the limit of small wavelength and globally incoherent light. We introduce quasi light fields to generalize the incoherent image formation process to coherent waves, offering resolution tradeoffs that surpass the traditional Fourier uncertainty principle by leveraging time-frequency distributions. We show that the number of sensors in a coherent imaging array defines a stable operating point relative to the phase noise. We introduce a digital phase tightening algorithm to reduce phase noise. We present a system identification framework for multiple-input multiple-output (MIMO) ultrasound imaging that generalizes existing approaches with time-varying filters. Our theoretical results enable the application of traditional techniques in incoherent imaging to coherent imaging, and vice versa. Our practical results suggest a methodology for designing millimeter-wave imaging systems. Our conclusions reinforce architectural principles governing transmitter and receiver design, the role of analog and digital circuity, and the tradeoff between data rate and data precision.Microsoft Research, MIT Lincoln Laboratory, and the C2S2 Focus Center, one of six research centers funded under the Focus Center Research Program (FCRP), a Semiconductor Research Corporation entity

Angle of Arrival Estimation Utilising Frequency Diverse Radio Antenna Arrays

The purpose of this research is to investigate a novel way of combining carrier signals that are transmitted successively over Multiple Frequencies (MF) and traditional metrics to improve AoA estimation. Every signal contains three metrics, amplitude, phase, and frequency. To achieve localisation, current systems utilise the metrics of amplitude (also known as Received Signal Strength (RSS)) and phase that resolves the AoA. However, the metric of frequency is mostly used with Orthogonal Frequency-Division Multiplexing (OFDM) to increase the number of RSS and AoA metrics, which is not optimal. This research answers two questions. Can the use of MF improve AoA estimation? Also, how can MF and traditional metrics be combined for AoA estimation? The aim is to prove that the metric of frequency can be utilised more optimally. Therefore, measurements of RSS and AoA are performed in different environments for MF. To perform these measurements, ten frequency diverse Software Defined Radios (SDRs) are employed. A novel technique to time/frequency synchronise the SDRs is developed and presented. Moreover, a ten element Uniform Linear Array (ULA) is designed, simulated and manufactured. The outcomes of this research are two novel algorithms for the MF AoA estimation of a carrier transmitter. Findings of the first algorithm show that the use of MF with the RSS metric performs equally with current systems that have a higher cost and complexity. The second algorithm that utilises MF with the AoA metric demonstrates a significant reduction in the AoA estimation error, compared to current systems. Specifically, for 50\% of the measured cases the AoA estimation error is reduced by 3.7 degrees, while for 95\% of the measured cases the AoA estimation error is reduced by 27 degrees. Hence, this research proves that MF with traditional metrics can reduce system complexity and greatly improve AoA estimation

Advanced Algebraic Concepts for Efficient Multi-Channel Signal Processing

Unsere moderne Gesellschaft ist Zeuge eines fundamentalen Wandels in der Art und Weise wie wir mit Technologie interagieren. Geräte werden zunehmend intelligenter - sie verfügen über mehr und mehr Rechenleistung und häufiger über eigene Kommunikationsschnittstellen. Das beginnt bei einfachen Haushaltsgeräten und reicht über Transportmittel bis zu großen überregionalen Systemen wie etwa dem Stromnetz. Die Erfassung, die Verarbeitung und der Austausch digitaler Informationen gewinnt daher immer mehr an Bedeutung. Die Tatsache, dass ein wachsender Anteil der Geräte heutzutage mobil und deshalb batteriebetrieben ist, begründet den Anspruch, digitale Signalverarbeitungsalgorithmen besonders effizient zu gestalten. Dies kommt auch dem Wunsch nach einer Echtzeitverarbeitung der großen anfallenden Datenmengen zugute. Die vorliegende Arbeit demonstriert Methoden zum Finden effizienter algebraischer Lösungen für eine Vielzahl von Anwendungen mehrkanaliger digitaler Signalverarbeitung. Solche Ansätze liefern nicht immer unbedingt die bestmögliche Lösung, kommen dieser jedoch häufig recht nahe und sind gleichzeitig bedeutend einfacher zu beschreiben und umzusetzen. Die einfache Beschreibungsform ermöglicht eine tiefgehende Analyse ihrer Leistungsfähigkeit, was für den Entwurf eines robusten und zuverlässigen Systems unabdingbar ist. Die Tatsache, dass sie nur gebräuchliche algebraische Hilfsmittel benötigen, erlaubt ihre direkte und zügige Umsetzung und den Test unter realen Bedingungen. Diese Grundidee wird anhand von drei verschiedenen Anwendungsgebieten demonstriert. Zunächst wird ein semi-algebraisches Framework zur Berechnung der kanonisch polyadischen (CP) Zerlegung mehrdimensionaler Signale vorgestellt. Dabei handelt es sich um ein sehr grundlegendes Werkzeug der multilinearen Algebra mit einem breiten Anwendungsspektrum von Mobilkommunikation über Chemie bis zur Bildverarbeitung. Verglichen mit existierenden iterativen Lösungsverfahren bietet das neue Framework die Möglichkeit, den Rechenaufwand und damit die Güte der erzielten Lösung zu steuern. Es ist außerdem weniger anfällig gegen eine schlechte Konditionierung der Ausgangsdaten. Das zweite Gebiet, das in der Arbeit besprochen wird, ist die unterraumbasierte hochauflösende Parameterschätzung für mehrdimensionale Signale, mit Anwendungsgebieten im RADAR, der Modellierung von Wellenausbreitung, oder bildgebenden Verfahren in der Medizin. Es wird gezeigt, dass sich derartige mehrdimensionale Signale mit Tensoren darstellen lassen. Dies erlaubt eine natürlichere Beschreibung und eine bessere Ausnutzung ihrer Struktur als das mit Matrizen möglich ist. Basierend auf dieser Idee entwickeln wir eine tensor-basierte Schätzung des Signalraums, welche genutzt werden kann um beliebige existierende Matrix-basierte Verfahren zu verbessern. Dies wird im Anschluss exemplarisch am Beispiel der ESPRIT-artigen Verfahren gezeigt, für die verbesserte Versionen vorgeschlagen werden, die die mehrdimensionale Struktur der Daten (Tensor-ESPRIT), nichzirkuläre Quellsymbole (NC ESPRIT), sowie beides gleichzeitig (NC Tensor-ESPRIT) ausnutzen. Um die endgültige Schätzgenauigkeit objektiv einschätzen zu können wird dann ein Framework für die analytische Beschreibung der Leistungsfähigkeit beliebiger ESPRIT-artiger Algorithmen diskutiert. Verglichen mit existierenden analytischen Ausdrücken ist unser Ansatz allgemeiner, da keine Annahmen über die statistische Verteilung von Nutzsignal und Rauschen benötigt werden und die Anzahl der zur Verfügung stehenden Schnappschüsse beliebig klein sein kann. Dies führt auf vereinfachte Ausdrücke für den mittleren quadratischen Schätzfehler, die Schlussfolgerungen über die Effizienz der Verfahren unter verschiedenen Bedingungen zulassen. Das dritte Anwendungsgebiet ist der bidirektionale Datenaustausch mit Hilfe von Relay-Stationen. Insbesondere liegt hier der Fokus auf Zwei-Wege-Relaying mit Hilfe von Amplify-and-Forward-Relays mit mehreren Antennen, da dieser Ansatz ein besonders gutes Kosten-Nutzen-Verhältnis verspricht. Es wird gezeigt, dass sich die nötige Kanalkenntnis mit einem einfachen algebraischen Tensor-basierten Schätzverfahren gewinnen lässt. Außerdem werden Verfahren zum Finden einer günstigen Relay-Verstärkungs-Strategie diskutiert. Bestehende Ansätze basieren entweder auf komplexen numerischen Optimierungsverfahren oder auf Ad-Hoc-Ansätzen die keine zufriedenstellende Bitfehlerrate oder Summenrate liefern. Deshalb schlagen wir algebraische Ansätze zum Finden der Relayverstärkungsmatrix vor, die von relevanten Systemmetriken inspiriert sind und doch einfach zu berechnen sind. Wir zeigen das algebraische ANOMAX-Verfahren zum Erreichen einer niedrigen Bitfehlerrate und seine Modifikation RR-ANOMAX zum Erreichen einer hohen Summenrate. Für den Spezialfall, in dem die Endgeräte nur eine Antenne verwenden, leiten wir eine semi-algebraische Lösung zum Finden der Summenraten-optimalen Strategie (RAGES) her. Anhand von numerischen Simulationen wird die Leistungsfähigkeit dieser Verfahren bezüglich Bitfehlerrate und erreichbarer Datenrate bewertet und ihre Effektivität gezeigt.Modern society is undergoing a fundamental change in the way we interact with technology. More and more devices are becoming "smart" by gaining advanced computation capabilities and communication interfaces, from household appliances over transportation systems to large-scale networks like the power grid. Recording, processing, and exchanging digital information is thus becoming increasingly important. As a growing share of devices is nowadays mobile and hence battery-powered, a particular interest in efficient digital signal processing techniques emerges. This thesis contributes to this goal by demonstrating methods for finding efficient algebraic solutions to various applications of multi-channel digital signal processing. These may not always result in the best possible system performance. However, they often come close while being significantly simpler to describe and to implement. The simpler description facilitates a thorough analysis of their performance which is crucial to design robust and reliable systems. The fact that they rely on standard algebraic methods only allows their rapid implementation and test under real-world conditions. We demonstrate this concept in three different application areas. First, we present a semi-algebraic framework to compute the Canonical Polyadic (CP) decompositions of multidimensional signals, a very fundamental tool in multilinear algebra with applications ranging from chemistry over communications to image compression. Compared to state-of-the art iterative solutions, our framework offers a flexible control of the complexity-accuracy trade-off and is less sensitive to badly conditioned data. The second application area is multidimensional subspace-based high-resolution parameter estimation with applications in RADAR, wave propagation modeling, or biomedical imaging. We demonstrate that multidimensional signals can be represented by tensors, providing a convenient description and allowing to exploit the multidimensional structure in a better way than using matrices only. Based on this idea, we introduce the tensor-based subspace estimate which can be applied to enhance existing matrix-based parameter estimation schemes significantly. We demonstrate the enhancements by choosing the family of ESPRIT-type algorithms as an example and introducing enhanced versions that exploit the multidimensional structure (Tensor-ESPRIT), non-circular source amplitudes (NC ESPRIT), and both jointly (NC Tensor-ESPRIT). To objectively judge the resulting estimation accuracy, we derive a framework for the analytical performance assessment of arbitrary ESPRIT-type algorithms by virtue of an asymptotical first order perturbation expansion. Our results are more general than existing analytical results since we do not need any assumptions about the distribution of the desired signal and the noise and we do not require the number of samples to be large. At the end, we obtain simplified expressions for the mean square estimation error that provide insights into efficiency of the methods under various conditions. The third application area is bidirectional relay-assisted communications. Due to its particularly low complexity and its efficient use of the radio resources we choose two-way relaying with a MIMO amplify and forward relay. We demonstrate that the required channel knowledge can be obtained by a simple algebraic tensor-based channel estimation scheme. We also discuss the design of the relay amplification matrix in such a setting. Existing approaches are either based on complicated numerical optimization procedures or on ad-hoc solutions that to not perform well in terms of the bit error rate or the sum-rate. Therefore, we propose algebraic solutions that are inspired by these performance metrics and therefore perform well while being easy to compute. For the MIMO case, we introduce the algebraic norm maximizing (ANOMAX) scheme, which achieves a very low bit error rate, and its extension Rank-Restored ANOMAX (RR-ANOMAX) that achieves a sum-rate close to an upper bound. Moreover, for the special case of single antenna terminals we derive the semi-algebraic RAGES scheme which finds the sum-rate optimal relay amplification matrix based on generalized eigenvectors. Numerical simulations evaluate the resulting system performance in terms of bit error rate and system sum rate which demonstrates the effectiveness of the proposed algebraic solutions

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion