Non-destructive testing (NDT) techniques are critical to ensure integrity and safety of engineered structures. Structural health monitoring (SHM) is considered as the next step in the field enabling continuous monitoring of structures. The first part of the thesis concerns NDT and SHM using guided waves in plates, or Lamb waves, to perform imaging of plate structures. The imaging is performed using a fixed active array setup covering a larger area of a plate. Current methods are based on conventional beamforming techniques that do not efficiently exploit the available data from the small arrays used for the purpose. In this thesis an adaptive signal processing approach based on the minimum variance distortionless response (MVDR) method is proposed to mitigate issues related to guided waves, such as dispersion and the presence of multiple propagating modes. Other benefits of the method include a significant increase in resolution. Simulation and experimental results show that the method outperforms current standard processing techniques. The second part of the thesis addresses transducer design issues for resonant ultrasound inspections. Resonant ultrasound methods utilize the shape and frequency of the object's natural modes of vibration to detect anomalies. The method considered in the thesis uses transducers that are acoustically coupled to the inspected structures. Changes in the transducer's electrical impedance are used to detect defects. The sensitivity that can be expected from such a setup is shown to highly depend on the transducer resonance frequency, as well as the working frequency of the instrument. Through simulations and a theoretical argumentation, optimal conditions to achieve high sensitivity are given.

[pt] A estimação de direção de chegada (DoA) é uma importante área de processamento de arranjos de sensores que é encontrada em uma ampla gama de aplicações de engenharia. Este fato, juntamente com o desenvolvimento da área de Compressed Sensing (CS) nos últimos anos, são a principal motivação desta dissertação. Nesta dissertação, é apresentada uma formulação do problema de estimação de direção de chegada como um problema de representação esparsa da sinal e vários algoritmos de recuperação esparsa são derivados e investigados para resolver o problema atual. Os algoritmos propostos são baseados na incorporação da informação prévia sobre o sinal esparso no processo de estimativa. Na primeira parte, nos concentramos no desenvolvimento de dois algoritmos Bayesianos , que se baseiam principalmente no algoritmo iterative hard thresholding (IHT). Devido ao desempenho inferior dos algoritmos convencionais de estimação de chegada em cenários com fontes correlacionadas, nós prestamos atenção especial ao desempenho dos algoritmos propostos nesta condição. Na segunda parte, o problema de otimização baseados na minimização da norma l1 é apresentado e um algoritmo bayesiano é proposto para resolver o problema chamado basis pursuit denoising (BPDN). Os resultados da simulação mostram que os estimadores Bayesianos superam os estimadores não Bayesianos e que a incorporação do conhecimento prévio da distribuição do sinal melhorou substancialmente o desempenho dos algoritmos. / [en] Direction of arrival (DoA) estimation is a key area of sensor array processing which is encountered in a broad range of important engineering applications. This fact together with the development of the Compressed Sensing (CS) area in the last years are the principal motivation of this thesis. In this dissertation, a formulation of the source localization problem as a sparse signal representation problem is presented and several sparse recovery algorithms are derived and investigated for solving the current problem. The proposed algorithms are based on the incorporation of the prior information about the sparse signal in the estimation process. In the first part, we focus on the development of two Bayesian greedy algorithms which are principally based on the iterative hard thresholding (IHT) algorithm. Due to the inferior performance of the conventional DoA estimation algorithm in scenarios with correlated sources, we pay special attention to the performance of the proposed algorithms under this condition. In the second part, the optimization problem using a l1 penalty is introduced and a Bayesian algorithm for solving the basis pursuit denoising problem is presented. Simulation results shows that Bayesian estimators which take into account the prior knowledge of the signal distribution outperform and improve substantially the performance of the non-Bayesian estimators.

Le développement des antennes modales, qui sont des antennes actives reconfigurables en diagramme de rayonnement mais pas en réseaux, a commencé pour pouvoir implémenter le dépointage électronique dans les téléphones portables. Cette solution est généralement réalisée par un réseau d’antennes, ce qui est très difficile, voire impossible, dans un téléphone portable. Le dépointage permettrait d’améliorer le bilan de liaison et d’augmenter le débit des données.Le premier objectif de cette thèse est donc de développer des antennes modales pour la norme LTE. Trois concepts d’antennes modales ont été présentés. Le premier est une antenne de type « folded loop » active reconfigurable par permutation des points d’alimentation et de retour à la masse. Le deuxième propose une architecture pour rassembler l’antenne principale et l’antenne diversité en bas du téléphone, tout en supportant le MIMO et l’agrégation de porteuses, deux des spécificités de la LTE. Le dernier optimise l’utilisation de la seconde antenne des systèmes LTE MIMO pour procéder au dépointage électronique.Ces antennes modales ne peuvent délivrer leur réel potentiel qu’en étant utilisées avec un algorithme de pilotage, qui choisira la configuration ou le mode approprié(e) à la situation. Le second objectif de cette thèse est donc le développement d’algorithmes intelligents de pilotage d’antennes modales. Le premier algorithme développé et breveté par la société Ethertronics a la particularité de prédire à chaque instant quel sera le meilleur mode à l’instant suivant, à l’aide de données en bande de base pour la LTE. Le second algorithme est déterministe et permet d’estimer et de suivre la direction d’arrivée du signal. Il sélectionne ensuite le mode qui possède le meilleur gain dans cette direction, dans le but d’améliorer le bilan de liaison. / The development of modal antennas which are radiation pattern reconfigurable active antennas, but not arrays, started in order to implement beam forming in cellphones. This solution is generally made with an antenna array, which is very difficult, even impossible, in a mobile phone. Beam forming could improve the link budget and increase the data rate. The first aim of this thesis is the development of modal antennas for LTE standard. Three modal antennas concepts were presented. The first one is a reconfigurable “folded loop” active antenna by permutation of feed and ground points. The second one is a solution to gather the main and the diversity antenna in the bottom part of the phone, and to support MIMO and carrier aggregation, which are two features of LTE. The last one optimizes the use of the second antenna in LTE MIMO systems in order to perform beam forming. These modal antennas will provide their real potential only by being used by a driving algorithm which will choose the best mode or configuration according to the situation. So, the second aim of this thesis is the development of smart modal antenna driving algorithms. The first algorithm, developed and patented by Ethertronics, has the specificity to predict, at each moment, which mode will be the best for the next moment, by using baseband data for LTE. The second algorithm is deterministic and can estimate and track the direction of arrival of the signal. Then, it selects the mode which has the best gain in this direction in order to improve the link budget.

En environnements hostiles, les signaux GNSS (Global Navigation Satellite System)peuvent être soumis à des risques de brouillages intentionnels. Basées sur un réseau d'antennes adaptatif, les solutions spatio-temporelles (STAP) ont déjà montré de bonnes performances de réjection des interférences. Toutefois, lorsque le module GNSS est placé sous les pales d'un hélicoptère, des effets non-stationnaires, appelés Rotor Blade Modulation (RBM), créés par les multiples réflexions du signal sur les pales du rotor, peuvent dégrader les techniques usuelles d’antibrouillage. Le signal utile GNSS n’est alors plus accessible. Le travail de la thèse consiste donc à élaborer un système de protection des signaux GNSS adapté à la RBM. Pour cela, un modèle innovant de multitrajets, adapté à ce type de phénomène, a été développé. La comparaison de simulations électromagnétiques représentatives et de mesures expérimentales sur hélicoptère EC-120 a permis de valider ce modèle. Celui-ci permet d'estimer, par maximum de vraisemblance, les paramètres de la contribution non-stationnaire du signal reçu. Enfin, l'association d'un algorithme de filtrage des multitrajets par projection oblique et d'un traitement STAP permet d'éliminer la contribution dynamique puis statique de l'interférence. Les simulations montrent que le signal utile GNSS est alors de nouveau exploitable. / In hostile environments, Global Navigation Satellite System (GNSS) can be disturbed by intentional jamming. Using antenna arrays, space-time adaptive algorithm (STAP) isone of the most efficient methods to deal with these threats. However, when a GNSS receiver is placed near rotating bodies, non-stationary effects called Rotor Blade Modulation (RBM) are created by the multipaths on the blades of the helicopter. They can degrade significantly the anti-jamming system and the signal of interest could belost. The work of the thesis is, consequently, to develop a GNSS protection system adapted to the RBM. In this way, an innovative multipath model, adapted to this phenomenon, has been developed. The model is then confirmed by comparison with a symptotic electromagnetic simulations and experiments conducted on an EC-120helicopter. Using a Maximum Likelihood algorithm, the parameters of the non-stationary part of the received signal have been estimated. And finally, the RBM anti-jamming solution, combining oblique projection algorithm and academic STAP, can mitigate dynamic and static contributions of interferences. In the end, the navigation information is available again.

Adaptive antenna systems (AAS's) are traditionally of interest only in radar and sonar applications. However, since the onset of the explosive growth in demand for wireless communications during the 1990's, researchers are giving increasing attention to the use of AAS technology to overcome practical challenges in providing the service. The main benefit of the technology lies in its ability to exploit the spatial domain, on top of the temporal and frequency domains, to improve on transceiver performance. This thesis presents a unified study on two classes of preprocessing techniques for uniform circular arrays (UCA's). UCA's are of interest because of their natural ability to provide a full azimuth (i.e. 360') coverage found in typical scenarios for sensor array applications, such as radar, sonar and wireless communications. The two classes of preprocessing techniques studied are the Davies transformation and the interpolated array transformations. These techniques yield a mathematically more convenient form - the Vandermonde form - for the array steering vector via a linear transformation. The Vandermonde form is useful for different applications such as direction-of-arrival (DOA) estimation and optimum or minimum variance distortionless response (MVDR) beamforming in correlated signal environment and beampattem synthesis. A novel interpolated array transformation is proposed to overcome limitations in the existing interpolated array transformations. A disadvantage of the two classes of preprocessing techniques for UCA's with omnidirectional elements is the lack of robustness in the transformed array steering vector to array imperfections under certain conditions. In order to mitigate the robustness problem, optimisation problems are formulated to modify the transformation matrices. / Suitable optimisation techniques are then applied to obtain more robust transformations. The improved transformations are shown to improve robustness but at the cost of larger transformation errors. The benefits of the robustification procedure are most apparent in DOA estimation. In addition to the algorithm level studies, the thesis also investigates the use of AAS technology with respect to two different third generation (3G) mobile communications systems: Enhanced Data rates for Global Evolution (EDGE) and Wideband Code Division Multiple Access (WCDMA). EDGE, or more generally GSM/EDGE Radio Access Network (GERAN), is the evolution of the widely successful GSM system to provide 3G mobile services in the existing radio spectrum. It builds on the TDMA technology of GSM and relies on improved coding and higher order modulation schemes to provide packet-based services at high data rates. WCDMA, on the other hand, is based on CDMA technology and is specially designed and streamlined for 3G mobile services. For WCDMA, a single-user approach to DOA estimation which utilises the user spreading code and the pulse-shaped chip waveform is proposed. It is shown that the proposed approach produces promising performance improvements. The studies with EDGE are concerned with the evaluation of a simple AAS at the system and link levels. / Results from, the system and link level simulations are presented to demonstrate the effectiveness of AAS technology in the new mobile communications system. Finally, it is noted that the WCDMA and EDGE link level simulations employ the newly developed COST259 directional channel model, which is capable of producing accurate channel realisations of macrocell environments for the evaluation of AAS's.

This thesis addresses a number of problems all related to parameter estimation in sensor array processing. The unifying theme is that some of these parameters are known before the measurements are acquired. We thus study how to improve the estimation of the unknown parameters by incorporating the knowledge of the known parameters; exploiting this knowledge successfully has the potential to dramatically improve the accuracy of the estimates. For covariance matrix estimation, we exploit that the true covariance matrix is Kronecker and Toeplitz structured. We then devise a method to ascertain that the estimates possess this structure. Additionally, we can show that our proposed estimator has better performance than the state-of-art when the number of samples is low, and that it is also efficient in the sense that the estimates have Cram\'er-Rao lower Bound (CRB) equivalent variance. In the direction of arrival (DOA) scenario, there are different types of prior information; first, we study the case when the location of some of the emitters in the scene is known. We then turn to cases with additional prior information, i.e.~when it is known that some (or all) of the source signals are uncorrelated. As it turns out, knowledge of some DOA combined with this latter form of prior knowledge is especially beneficial, giving estimators that are dramatically more accurate than the state-of-art. We also derive the corresponding CRBs, and show that under quite mild assumptions, the estimators are efficient. Finally, we also investigate the frequency estimation scenario, where the data is a one-dimensional temporal sequence which we model as a spatial multi-sensor response. The line-frequency estimation problem is studied when some of the frequencies are known; through experimental data we show that our approach can be beneficial. The second frequency estimation paper explores the analysis of pulse spin-locking data sequences, which are encountered in nuclear resonance experiments. By introducing a novel modeling technique for such data, we develop a method for estimating the interesting parameters of the model. The technique is significantly faster than previously available methods, and provides accurate estimation results. / Denna doktorsavhandling behandlar parameterestimeringsproblem inom flerkanals-signalbehandling. Den gemensamma förutsättningen för dessa problem är att det finns information om de sökta parametrarna redan innan data analyseras; tanken är att på ett så finurligt sätt som möjligt använda denna kunskap för att förbättra skattningarna av de okända parametrarna. I en uppsats studeras kovariansmatrisskattning när det är känt att den sanna kovariansmatrisen har Kronecker- och Toeplitz-struktur. Baserat på denna kunskap utvecklar vi en metod som säkerställer att även skattningarna har denna struktur, och vi kan visa att den föreslagna skattaren har bättre prestanda än existerande metoder. Vi kan också visa att skattarens varians når Cram\'er-Rao-gränsen (CRB). Vi studerar vidare olika sorters förhandskunskap i riktningsbestämningsscenariot: först i det fall då riktningarna till ett antal av sändarna är kända. Sedan undersöker vi fallet då vi även vet något om kovariansen mellan de mottagna signalerna, nämligen att vissa (eller alla) signaler är okorrelerade. Det visar sig att just kombinationen av förkunskap om både korrelation och riktning är speciellt betydelsefull, och genom att utnyttja denna kunskap på rätt sätt kan vi skapa skattare som är mycket noggrannare än tidigare möjligt. Vi härleder även CRB för fall med denna förhandskunskap, och vi kan visa att de föreslagna skattarna är effektiva. Slutligen behandlar vi även frekvensskattning. I detta problem är data en en-dimensionell temporal sekvens som vi modellerar som en spatiell fler-kanalssignal. Fördelen med denna modelleringsstrategi är att vi kan använda liknande metoder i estimatorerna som vid sensor-signalbehandlingsproblemen. Vi utnyttjar återigen förhandskunskap om källsignalerna: i ett av bidragen är antagandet att vissa frekvenser är kända, och vi modifierar en existerande metod för att ta hänsyn till denna kunskap. Genom att tillämpa den föreslagna metoden på experimentell data visar vi metodens användbarhet. Det andra bidraget inom detta område studerar data som erhålls från exempelvis experiment inom kärnmagnetisk resonans. Vi introducerar en ny modelleringsmetod för sådan data och utvecklar en algoritm för att skatta de önskade parametrarna i denna modell. Vår algoritm är betydligt snabbare än existerande metoder, och skattningarna är tillräckligt noggranna för typiska tillämpningar. / <p>QC 20131115</p>

Les premières recherches en détection, localisation et séparation de signaux remontent au début du 20ème siècle. Ces recherches sont d’actualité encore aujourd’hui, notamment du fait de la croissance rapide des systèmes de communications constatée ces deux dernières décennies. Par ailleurs, la littérature du domaine consacre très peu d’études relatives à certains contextes jugés difficiles dont certains sont traités dans cette thèse. Ce travail porte sur la localisation de signaux par détection des temps d’arrivée ou estimation des directions d’arrivée et sur la séparation de sources dépendantes ou à module constant. L’idée principale est de tirer profit de certaines informations a priori disponibles sur les signaux sources telles que la parcimonie, la cyclostationarité, la non-circularité, le module constant, la structure autoregressive et les séquences pilote dans un contexte coopératif. Une première partie détaille trois contributions : (i) un nouveau détecteur pour l’estimation des temps d’arrivée basé sur la minimisation de la probabilité d’erreur ; (ii) une estimation améliorée de la puissance du bruit, basée sur les statistiques d’ordre ; (iii) une quantification de la précision et de la résolution de l’estimation des directions d’arrivée au regard de certains a priori considérés sur les sources. Une deuxième partie est consacrée à la séparation de sources exploitant différentes informations sur celles-ci : (i) la séparation de signaux de communication à module constant ; (ii) la séparation de sources dépendantes connaissant la nature de la dépendance et (iii) la séparation de sources autorégressives dépendantes connaissant la structure autorégressive. / Signal detection, localization, and separation problems date back to the beginning of the twentieth century. Nowadays, this subject is still a hot topic receiving more and more attention, notably with the rapid growth of wireless communication systems that arose in the last two decades and it turns out that many challenging aspects remain poorly addressed by the available literature relative to this subject. This thesis deals with signal detection, localization using temporal or directional measurements, and separation of dependent source signals. The main objective is to make use of some available priors about the source signals such as sparsity, cyclo-stationarity, non-circularity, constant modulus, autoregressive structure or training sequences in a cooperative framework. The first part is devoted to the analysis of (i) signal’s time-of-arrival estimation using a new minimum error rate based detector, (ii) noise power estimation using an improved order-statistics estimator and (iii) side information impact on direction-of-arrival estimation accuracy and resolution. In the second part, the source separation problem is investigated at the light of different priors about the original sources. Three kinds of prior have been considered : (i) separation of constant modulus communication signals, (ii) separation of dependent source signals knowing their dependency structure and (iii) separation of dependent autoregressive sources knowing their autoregressive structure.

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