Based on different sampling theorem, for example classic Shannon’s sampling theorem and Papoulis’ generalized sampling theorem, signals are processed by the sampling devices without loss of information. As an interface between radio receiver front-ends and digital signal processing blocks, sampling devices play a dominant role in digital radio communications. Under the concept of Software Defined Radio (SDR), radio systems are going through the second evolution that mixes analog, digital and software technologies in modern radio designs. One design goal of SDR is to put the A/D converter as close as possible to the antenna. BandPass Sampling (BPS) enables one to have an interface between the RF or the higher IF signal and the A/D converter, and it might be a solution to SDR. However, three sources of performance degradation present in BPS systems, harmful signal spectral overlapping, noise aliasing and sampling timing jitter, hinder the conventional BPS theory from practical circuit implementations. In this thesis work, Generalized Quadrature BandPass Sampling (GQBPS) is first invented and comprehensively studied with focus on the noise aliasing problem. GQBPS consists of both BPS and FIR filtering that can use either real or complex coefficients. By well-designed FIR filtering, GQBPS can also perform frequency down-conversion in addition to noise aliasing reduction. GQBPS is a nonuniform sampling method in most cases. With respect to real circuit implementations, uniform sampling is easier to be realized compared to nonuniform sampling. GQBPS has been also extended to Generalized Uniform BandPass Sampling (GUBPS). GUBPS shares the same property of noise aliasing suppression as GQBPS besides that the samples are uniformly spaced. Due to the moving average operation of FIR filtering, the effect of sampling jitter is also reduced to a certain degree in GQBPS and GUBPS. By choosing a suitable sampling rate, harmful signal spectral overlapping can be avoided. Due to the property of quadrature sampling, the “self image” problem caused by I/Q mismatches is eliminated. Comprehensive theoretical analyses and program simulations on GQBPS and GUBPS have been done based on a general mathematic model. Circuit architecture to implementing GUBPS in Switched-Capacitor circuit technique has been proposed and analyzed. To improve the selectivity at the sampling output, FIR filtering is extended by adding a 1st order complex IIR filter in the implementation. GQBPS and GUBPS operate in voltage-mode. Besides voltage sampling, BPS can also be realized by charge sampling in current-mode. Most other research groups in this area are focusing on bandpass charge sampling. However, the theoretical analysis shows that our GQBPS and GUBPS in voltage mode are more efficient to suppress noise aliasing as compared to bandpass charge sampling with embedded filtering. The aliasing bands of sampled-data spectrum are always weighted by continuous-frequency factors for bandpass charge sampling with embedded filtering while discrete-frequency factors for GQBPS and GUBPS. The transmission zeros of intrinsic filtering will eliminate the corresponding whole aliasing bands of both signal and noise in GQBPS and GUBPS, while it will only cause notches at a limited set of frequencies in bandpass charge sampling. In addition, charge sampling performs an intrinsic continuous-time sinc function that always includes lowpass filtering. This is a drawback for a bandpass input signal. / QC 20100921

We present the design, development, and test of three novel, distinct automatic target recognition (ATR) systems for the recognition of airplanes and, more specifically, non-cooperative airplanes, i.e. airplanes that do not provide information when interrogated, in the framework of passive bistatic radar systems. Passive bistatic radar systems use one or more illuminators of opportunity (already present in the field), with frequencies up to 1 GHz for the transmitter part of the systems considered here, and one or more receivers, deployed by the persons managing the system, and not co-located with the transmitters. The sole source of information are the signal scattered on the airplane and the direct-path signal that are collected by the receiver, some basic knowledge about the transmitter, and the geometrical bistatic radar configuration. The three distinct ATR systems that we built respectively use the radar images, the bistatic complex radar cross-section (BS-RCS), and the bistatic radar cross-section (BS-RCS) of the targets. We use data acquired either on scale models of airplanes placed in an anechoic, electromagnetic chamber or on real-size airplanes using a bistatic testbed consisting of a VOR transmitter and a software-defined radio (SDR) receiver, located near Orly airport, France. We describe the radar phenomenology pertinent for the problem at hand, as well as the mathematical underpinnings of the derivation of the bistatic RCS values and of the construction of the radar images.For the classification of the observed targets into pre-defined classes, we use either extremely randomized trees or subspace methods. A key feature of our approach is that we break the recognition problem into a set of sub-problems by decomposing the parameter space, which consists of the frequency, the polarization, the aspect angle, and the bistatic angle, into regions. We build one recognizer for each region. We first validate the extra-trees method on the radar images of the MSTAR dataset, featuring ground vehicles. We then test the method on the images of the airplanes constructed from data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.99.We test the subspace methods on the BS-CRCS and on the BS-RCS of the airplanes extracted from the data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.98, with variations according to the frequency band, the polarization, the sector of aspect angle, the sector of bistatic angle, and the number of (Tx,Rx) pairs used. The ATR system deployed in the field gives a probability of correct recognition of $0.82$, with variations according to the sector of aspect angle and the sector of bistatic angle.

Software Defined Radios (SDRs) and Cognitive Radios (CRs) pave the way for next-generation radio technology. They promise versatility, flexibility and cognition which can revolutionize communications systems. However they present greater challenges to the design of radio frequency (RF) front-ends. RF front-ends for the radios in use today are narrow-band in their frequency response and are optimized and tuned to the carrier frequency of interest. SDRs and CRs demand front-ends which are versatile, configurable, tunable and be capable of transmitting and receiving signals with different bandwidths and modulation schemes. Integrating power amplifiers (PAs) with transmitters in CMOS has many advantages and challenges. This thesis deals with the design of an RF transmitter front-end for SDRs and CRs in CMOS. The thesis begins with an introduction to SDRs and the requirements they place on transmitters and the challenges involved in designing them in CMOS. After a brief overview of the existing techniques, the proposed architecture is presented and explained. A digitally intensive transmitter solution is proposed. The transmitter covers a wide frequency range of 750 MHz to 2.5 GHz. The inputs to the proposed transmitter are in-phase and quadrature (I & Q) data bit streams. Multiple stages of up-sampling and filtering are used to remove all spurs in the spectrum such that only the harmonics of the carrier remain. Differential rail-to-rail quadrature clocks are generated from a continuous wave signal at twice the carrier frequency. The clocks are corrected for their duty cycle and quadrature impairments. The heart of the transmitter is an integrated reconfigurable CMOS power amplifier (PA). A methodology to design reconfigurable Class E PAs with a series fixed inductor has been presented. A CMOS power amplifier that can span a wide frequency range with sufficient output power and efficiency, supporting varying envelope complex modulation signals, with good linearity has been designed. Digital pre-distortion (DPD) is used to linearize the PA. The full transmitter and the clock correction blocks have been designed and fabricated in a commercial 130-nm CMOS process and experimentally characterized. The PA delivers a maximum power of 13 dBm with an efficiency of 27% at 1 GHz. While transmitting a 16-QAM signal at 1 GHz, the measured EVM is 4%. It delivers a maximum power of around 11-13 dBm from 750 MHz to 1.5 GHz and up to 6.5 dBm of power till 2.5 GHz. Comparing the proposed system with recently published literature, it can be seen that the proposed design is one of the very few transmitters which has an integrated matching network, tunable across the frequency range. The proposed PA produces the highest output power and with largest efficiency for systems with on-chip output networks.

<p dir="ltr">This Research focuses on developing artificial intelligence tools to detect and mitigate cyber-attacks targeting unmanned aerial vehicles. </p>

Dans cette thèse, nous présentons la conception, le développement et le test de trois systèmes de reconnaissance automatique de cibles (ATR) visant à reconnaître des avions non-coopératifs, c’est-à-dire des avions ne fournissant par leur identité, en utilisant des signaux de radars passifs bistatiques. Les radars passifs bistatiques utilisent un ou plusieurs émetteurs d’opportunité (déjà présents sur le terrain), avec des fréquences allant jusqu’à 1 GHz pour les émetteurs considérés ici, et un ou plusieurs récepteurs déployés par le gestionnaire du système et non-colocalisés avec les émetteurs. Les seules informations utilisées sont les signaux réfléchis sur les avions et les signaux directement reçus qui sont tous les deux collectés par le récepteur, quelques informations concernant l’émetteur, et la configuration géométrique du radar bistatique.Les trois systèmes ATR que nous avons construits utilisent respectivement les images radar, les surfaces équivalentes radar (SER) complexes bistatiques et les SER réelles bistatiques. Nous utilisons des données acquises soit sur des modèles d’avions placés en chambre anéchoique à l’ONERA, soit sur des avions réels en utilisant un banc d’essai bistatique consistant en un émetteur VOR et un récepteur basé sur la radio-logicielle (SDR), et que nous avons déployé aux alentours de l’aéroport d’Orly. Nous décrivons d’abord la phénoménologie radar pertinente pour notre problème ainsi que les fondements mathématiques pour la dérivation de la SER bistatique d’un objet, et pour la construction d’images radar d’un objet.Nous utilisons deux méthodes pour la classification de cibles en classes prédéfinies : les arbres extrêmement aléatoires (extra-trees) et les méthodes de sous-espaces. Une caractéristique-clé de notre approche est que nous divisons le problème de reconnaissance global en un ensemble de sous-problèmes par décomposition de l’espace des paramètres (fréquence, polarisation, angle d’aspect et angle bistatique) en régions. Nous construisons un classificateur par région.Nous validons en premier lieu la méthode des extra-trees sur la base de données MSTAR, composée d’images radar de véhicules terrestres. Ensuite, nous testons cette méthode sur des images radar d’avions que nous avons construites à partir des données acquises en chambre anéchoique. Nous obtenons un pourcentage de classification allant jusqu’à 99%. Nous testons ensuite la méthode de sous-espaces sur les SER bistatiques (complexes et réelles) des avions que nous avons extraits des données de chambre anéchoique. Nous obtenons un pourcentage de classification allant jusqu’à 98%, avec des variations suivant la fréquence, la polarisation, l’angle d’aspect, l’angle bistatique et le nombre de paires émetteur-récepteur utilisées. Nous testons enfin la méthode de sous-espaces sur les SER bistatiques (réelles) extraites des signaux acquis par le banc d’essai déployé à Orly. Nous obtenons une probabilité de classification de 82%, avec des variations suivant l’angle d’aspect et l’angle bistatique. On a donc démontré dans cette thèse que l’on peut reconnaitre des cibles aériennes à partir de leur SER acquise en utilisant des signaux de radars passifs bistatiques. / We present the design, development, and test of three novel, distinct automatic target recognition (ATR) systems for the recognition of airplanes and, more specifically, non-cooperative airplanes, i.e. airplanes that do not provide information when interrogated, in the framework of passive bistatic radar systems. Passive bistatic radar systems use one or more illuminators of opportunity (already present in the field), with frequencies up to 1 GHz for the transmitter part of the systems considered here, and one or more receivers, deployed by the persons managing the system, and not co-located with the transmitters. The sole source of information are the signal scattered on the airplane and the direct-path signal that are collected by the receiver, some basic knowledge about the transmitter, and the geometrical bistatic radar configuration. The three distinct ATR systems that we built respectively use the radar images, the bistatic complex radar cross-section (BS-RCS), and the bistatic radar cross-section (BS-RCS) of the targets. We use data acquired either on scale models of airplanes placed in an anechoic, electromagnetic chamber or on real-size airplanes using a bistatic testbed consisting of a VOR transmitter and a software-defined radio (SDR) receiver, located near Orly airport, France. We describe the radar phenomenology pertinent for the problem at hand, as well as the mathematical underpinnings of the derivation of the bistatic RCS values and of the construction of the radar images.For the classification of the observed targets into pre-defined classes, we use either extremely randomized trees or subspace methods. A key feature of our approach is that we break the recognition problem into a set of sub-problems by decomposing the parameter space, which consists of the frequency, the polarization, the aspect angle, and the bistatic angle, into regions. We build one recognizer for each region. We first validate the extra-trees method on the radar images of the MSTAR dataset, featuring ground vehicles. We then test the method on the images of the airplanes constructed from data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.99.We test the subspace methods on the BS-CRCS and on the BS-RCS of the airplanes extracted from the data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.98, with variations according to the frequency band, the polarization, the sector of aspect angle, the sector of bistatic angle, and the number of (Tx,Rx) pairs used. The ATR system deployed in the field gives a probability of correct recognition of $0.82$, with variations according to the sector of aspect angle and the sector of bistatic angle.