This manuscript concerns the performance analysis in signal processing and consists into two parts : First, we study the lower bounds in characterizing and predicting the estimation performance in terms of mean square error (MSE). The lower bounds on the MSE give the minimum variance that an estimator can expect to achieve and it can be divided into two categories depending on the parameter assumption: the so-called deterministic bounds dealing with the deterministic unknown parameters, and the so-called Bayesian bounds dealing with the random unknown parameter. Particularly, we derive the closed-form expressions of the lower bounds for two applications in two different fields: (i) The first one is the target localization using the multiple-input multiple-output (MIMO) radar in which we derive the lower bounds in the contexts with and without modeling errors, respectively. (ii) The other one is the pulse phase estimation of X-ray pulsars which is a potential solution for autonomous deep space navigation. In this application, we show the potential universality of lower bounds to tackle problems with parameterized probability density function (pdf) different from classical Gaussian pdf since in X-ray pulse phase estimation, observations are modeled with a Poisson distribution. Second, we study the statistical resolution limit (SRL) which is the minimal distance in terms of the parameter of interest between two signals allowing to correctly separate/estimate the parameters of interest. More precisely, we derive the SRL in two contexts: array processing and MIMO radar by using two approaches based on the estimation theory and information theory. We also present in this thesis the usefulness of SRL in optimizing the array system.

In this thesis novel feedback attitude control algorithms and attitude estimation algorithms are developed for a three-axis stabilised spacecraft attitude control system. The spacecraft models considered include a rigid-body spacecraft equipped with (i) external control torque devices, and (ii) a redundant reaction wheel configuration. The attitude sensor suite comprises a three-axis magnetometer and three-axis rate gyroscope assembly. The quaternion parameters (also called Euler symmetric parameters), which globally avoid singularities but are subject to a unity-norm constraint, are selected as the primary attitude coordinates. There are four novel contributions presented in this thesis. The first novel contribution is the development of a robust control strategy for spacecraft attitude tracking maneuvers, in the presence of dynamic model uncertainty in the spacecraft inertia matrix, actuator magnitude constraints, bounded persistent external disturbances, and state estimation error. The novel component of this algorithm is the incorporation of state estimation error into the stability analysis. The proposed control law contains a parameter which is dynamically adjusted to ensure global asymptotic stability of the overall closedloop system, in the presence of these specific system non-idealities. A stability proof is presented which is based on Lyapunov's direct method, in conjunction with Barbalat's lemma. The control design approach also ensures minimum angular path maneuvers, since the attitude quaternion parameters are not unique. The second novel contribution is the development of a robust direct adaptive control strategy for spacecraft attitude tracking maneuvers, in the presence of dynamic model uncertainty in the spacecraft inertia matrix. The novel aspect of this algorithm is the incorporation of a composite parameter update strategy, which ensures global exponential convergence of the closed-loop system. A stability proof is presented which is based on Lyapunov's direct method, in conjunction with Barbalat's lemma. The exponential convergence results provided by this control strategy require persistently exciting reference trajectory commands. The control design approach also ensures minimum angular path maneuvers. The third novel contribution is the development of an optimal control strategy for spacecraft attitude maneuvers, based on a rigid body spacecraft model including a redundant reaction wheel assembly. The novel component of this strategy is the proposal of a performance index which represents the total electrical energy consumed by the reaction wheel over the maneuver interval. Pontraygin's minimum principle is applied to formulate the necessary conditions for optimality, in which the control torques are subject to timevarying magnitude constraints. The presence of singular sub-arcs in the statespace and their associated singular controls are investigated using Kelley's necessary condition. The two-point boundary-value problem (TPBVP) is formulated using Pontrayagin's minimum principle. The fourth novel contribution is an attitude estimation algorithm which estimates the spacecraft attitude parameters and sensor bias parameters from three-axis magnetometer and three-axis rate gyroscope measurement data. The novel aspect of this algorithm is the assumption that the state filtering probability density function (PDF) is Gaussian distributed. This Gaussian PDF assumption is also applied to the magnetometer measurement model. Propagation of the filtering PDF between sensor measurements is performed using the Fokker-Planck equation, and Bayes theorem incorporates measurement update information. The use of direction cosine matrix elements as the attitude coordinates avoids any singularity issues associated with the measurement update and estimation error covariance representation.

Bien que le traitement conjoint des mesures multimodales soit supposé conduire à de meilleures performances que celles obtenues en exploitant une seule modalité ou plusieurs modalités indépendamment, il existe des exemples en littérature qui prouvent que c'est pas toujours vrai. Dans cette thèse, nous analysons rigoureusement, en termes d'information mutuelle et d'erreur d'estimation, les différentes situations de l'analyse multimodale afin de déterminer les conditions conduisant à des performances optimales.Dans la première partie, nous considérons le cas simple de deux ou trois modalités, chacune étant associée à la mesure bruitée d'un signal, avec des liens entre modalités matérialisés par les corrélations entre les parties utiles du signal et par les corrélations les bruits. Nous montrons comment les performances obtenues sont améliorées avec l'exploitation des liens entre les modalités. Dans la seconde partie, nous étudions l'impact sur les performances d'erreurs sur les liens entre modalités. Nous montrons que ces fausses hypothèses dégradent les performances, qui peuvent alors devenir inférieure à celles atteintes avec une seule modalité.Dans le cas général, nous modélisons les multiples modalités comme un canal gaussien bruité. Nous étendons alors des résultats de la littérature en considérant l'impact d'erreurs sur les densités de probabilité du signal et du bruit sur l'information transmise par le canal. Nous analysons ensuite cette relation dans la cas d'un modèle simple de deux modalités. Nos résultats montrent en particulier le fait inattendu qu'une double inadéquation du bruit et du signal peuvent parfois se compenser et ainsi conduire à de très bonnes performances. / The joint processing of multimodal measurements is supposed to lead to better performances than those obtained using a single modality or several modalities independently. However, in literature, there are examples that show that is not always true. In this thesis, we analyze, in terms of mutual information and estimation error, the different situations of multimodal analysis in order to determine the conditions to achieve the optimal performances.In the first part, we consider the simple case of two or three modalities, each associated with noisy measurement of a signal. These modalities are linked through the correlations between the useful parts of the signal and the correlations between the noises. We show that the performances are improved if the links between the modalities are exploited. In the second part, we study the impact on performance of wrong links between modalities. We show that these false assumptions decline the performance, which can become lower than the performance achieved using a single modality.In the general case, we model the multiple modalities as a noisy Gaussian channel. We then extend literature results by considering the impact of the errors on signal and noise probability densities on the information transmitted by the channel. We then analyze this relationship in the case of a simple model of two modalities. Our results show in particular the unexpected fact that a double mismatch of the noise and the signal can sometimes compensate for each other, and thus lead to very good performances.

Orientador: Prof. Dr.Fernando Luis Semião da Silva / Tese (doutorado) - Universidade Federal do ABC, Programa de Pós-Graduação em Física, 2015. / A presente tese de doutorado tem como principal objetivo empregar luz quantizada para o controle e medida em sistemas complexos como em um conjunto de cromóforos, e em um condensado de Bose-Einstein. Em especial, desenvolvemos formalismos de pulsos e equações mestras aplicáveis a esses sistemas e utilizamos teoria de estimativa quântica para determinar quantidades atômicas relevantes de maneira indireta (medindo propriedades da luz). Também empregamos técnicas variadas para obtenção de equações mestras para estudar diferentes problemas envolvendo interação de luz quântica com matéria. Em um caso, obtivemos uma equação mestra microscópica para o estudo de dois sistemas de dois níveis acoplados no espaço livre, e mostramos como a equação mestra microscópica desse sistema se distingue de equações fenomenológicas para o mesmo. Por fim, também discutimos a obtenção de uma equação mestra para o estudo de transferência de emaranhamento entre o campo eletromagnético quantizado e complexos Fenna-Matthews-Olson localizados em cavidades óticas distintas. / The main aim of the present Ph.D. thesis is to employ quantized light to control and measurement of complex systems such as a set of chromophores and a Bose- Einstein condensate in a double well. Specifically, we develop a formalism for pulses and master equations that can be applied to those systems and use quantum estimation theory to get information on relevant parameters in atomic system in a indirect way (measuring light properties). We also employ varied techniques for the obtention of master equations to study different problems involving the interaction of quantum light and matter. We obtained a microscopic master equation for the study of two coupled two-level systems in the free space, and we showed how the microscopic master equation of this system distinguish from the phenomenological ones. Finally, we also discuss the obtention of a master equation for the study of the transference of entanglement between the quantized electromagnetic field and the Fenna-Matthews-Olson complex localized in distinct optical cavities.

Orientadores: Renato da Rocha Lopes, João Marcos Travassos Romano / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-13T19:18:48Z (GMT). No. of bitstreams: 1 Loiola_MuriloBellezoni_D.pdf: 1970092 bytes, checksum: 28591fb8ccc8eb5f6eb64dfa626f4241 (MD5) Previous issue date: 2009 / Resumo: Neste trabalho utilizamos filtros de Kalman para estimar canais de comunicação sem fio variantes no tempo em sistemas com múltiplas entradas e múltiplas saídas. Primeiramente, propusemos um estimador ótimo (no sentido de minimização do erro quadrático médio de estimação) para rastrear canais planos em sistemas utilizando códigos espaço-temporais ortogonais por blocos. Graças à ortogonalidade destes códigos, foi possível simplificar as equações do filtro de Kalman. Mostramos que as estimativas fornecidas pelo estimador proposto correspondem a somas ponderadas de estimativas instantâneas de máxima verossimilhança do canal. Ainda para este sistema, propusemos um filtro de Kalman em estado estacionário para modulações de módulo constante. O filtro em estado estacionário tem desempenho semelhante ao do filtro de Kalman ótimo, embora necessite apenas de uma fração dos cálculos envolvidos. Em seguida, propusemos um receptor baseado no filtro de Kalman estendido para realizar conjuntamente as tarefas de estimação de canais seletivos em freqüência e detecção de sinais em sistemas com múltiplas entradas, múltiplas saídas (MIMO, do inglês multiple-input, multiple-output) e multiplexação espacial. Por fim, adaptamos este estimador conjunto para incorporá-lo a um receptor turbo. Desta maneira, o estimador conjunto pode aproveitar a redundância introduzida pela codificação de canal para aprimorar as estimativas dos coeficientes do canal e dos símbolos transmitidos por meio de um processo iterativo / Abstract: In this work we use Kalman filters to estimate time-varying wireless channels in multiple-input, multiple-output (MIMO) systems. First, we propose an optimal estimator (in the minimum mean squared error sense) to track flat channels in orthogonal space-time block coded systems. Due to the orthogonality inherent to these codes, the Kalman filter equations can be simplified. We also show that the channel estimates provided by the proposed estimator correspond to weighted sums of instantaneous maximum likelihood channel estimates. For constant modulus signal constellations, we propose a steady-state Kalman filter. The proposed steady-state Kalman filter suffers negligible performance degradation compared to the optimal Kalman filter while requiring just a fraction of its complexity. After that, we propose an extended Kalman filter-based receiver that jointly performs the estimation of time-varying frequency-selective MIMO channels and the detection of transmitted signals in spatial multiplexing systems. Finally, we adapt this joint estimator to a turbo receiver. Therefore, the joint estimator can benefit from the error correction capabilities of channel codes to iteratively improve channel and signal estimates / Doutorado / Telecomunicações e Telemática / Doutor em Engenharia Elétrica

Orientador: Amauri Lopes / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-14T21:35:53Z (GMT). No. of bitstreams: 1 Gomes_MarcoAurelioCazarotto_M.pdf: 1012758 bytes, checksum: 0c2ca6c09e4123b277735dca0f50a107 (MD5) Previous issue date: 2009 / Resumo: Abordamos o problema de estimação de direção de chegada (DOA) de ondas planas usando um arranjo de sensores. Na literatura encontramos diversos estimadores para DOA, porém estamos considerando apenas os estimadores de Máxima Verossimilhança (ML) que geram candidatas à estimativa DOA e selecionam as melhores através do critério ML. Também estamos interessados em situações em que o espaçamento angular entre as fontes de sinal é pequeno e a relação sinal-ruído é baixa. Nesse caso temos uma degradação de desempenho associada ao efeito de limiar. Mostramos que este problema pode ser amenizado reduzindo o ruído presente na matriz de covariância dos dados recebidos (snapshots) utilizada para a seleção das candidatas. Propomos então modificar o processo de seleção de candidatas, utilizando uma nova matriz de covariância dos snapshots, calculada após uma filtragem ótima dos dados através de um filtro FIR multibanda. Propomos também modificar a função custo ML para adequá-la às dimensões da matriz de covariância filtrada e para isso apresentamos 3 opções de modificação. As simulações mostram que nossa proposta tem melhor desempenho que os métodos conhecidos, reduzindo significativamente a relação sinal-ruído de limiar. / Abstract: We approached the estimation of direction of arrival (DOA) of plane waves using an array of sensors. In the literature there are several DOA estimators, but we considered only the maximum likelihood (ML) estimators that generate candidates for DOA estimation and select the best one through an ML criterion. We also considered situations where the signal sources are spatially closely spaced and the signal-to-noise ratio is low. In these cases a performance degradation associated with the threshold effect occur. We demonstrated that we can improve the estimation performance by reducing the noise in the received data covariance matrix used to select the candidates. Then we proposed to modify the selection process using a new data covariance matrix, computed after an optimum multiband FIR filtering of the received data. We also proposed to modify the ML cost function to adapt it to the dimensions of the new covariance matrix and we considered 3 alternatives of modification. Some simulations showed that our proposal has better performance than known DOA methods, significantly reducing the threshold SNR. / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica

Orientadores: Ronaldo Dias, Nancy Lopes Garcia / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-21T02:16:44Z (GMT). No. of bitstreams: 1 Alcantara_AdeiltonPedrode_D.pdf: 7403994 bytes, checksum: a1b986bd21c825efb7bc7ecbb40c550f (MD5) Previous issue date: 2012 / Resumo: Esta tese tem por objetivo propor uma nova metodologia baseada no método da expansão por bases B-splines e suas variantes para estimação não paramétrica da função intensidade...Observação: O resumo, na íntegra, poderá ser visualizado no texto completo da tese digital / Abstract: The main goal of this thesis is to propose a new methodology based on the method of expansion by B-splines bases for non-parametric estimate of the intensity function...Note: The complete abstract is available with the full electronic document / Doutorado / Estatistica / Doutor em Estatística

Ce manuscrit est dédié à la résolution du problème de localisation dans les réseaux de capteurs sans fil mobiles. Les méthodes développées se basent principalement sur des caractéristiques de fingerprints ainsi que sur des informations de mobilité. Les premières s'attaquent aux valeurs de RSSI entre capteurs tandis que les deuxièmes prennent en considération la mobilité des capteurs mesurée à l'aide d'accéléromètres et de gyroscopes. La combinaison des données collectées est effectuée dans le cadre de l'analyse par intervalles, ou bien du filtrage de Kalman. Les travaux proposés introduisent des modèles de mobilité d'ordres un, deux ou trois, permettant d'approximer au mieux les trajectoires des capteurs à l'aide des accélérations mesurées. Ceux-là sont couplés à l'algorithme des K plus proches voisins, d'abord dans un système centralisé. Ensuite, les modèles de mobilités sont améliorés pour prendre en compte les rotations des nœuds. Une méthode de localisation décentralisée est également proposée dans ce qui suit, s'adaptant au mécanisme fonctionnel des réseaux de capteurs de grande échelle. Enfin, ce manuscrit propose une méthode de zonage visant à déterminer les zones dans lesquelles les capteurs résident. La méthode proposée aborde le problème de zonage en utilisant à la fois la théorie des fonctions de croyance et l'analyse par intervalles / This thesis is dedicated to solve the localization problem in mobile wireless sensor networks. It works mainly with fingerprints features and inertial movements information. The former tackles the RSSIs values between sensors while the latter deals with the objets movement attitude by using accelerometer and gyroscope. The combination of both information is performed in terms of interval analysis, or Kalman filtering. The proposed work introduces three orders mobility models to approximate nodes trajectories using accelerations, combined then to the weighted K nearest neighbors algorithm in a centralized scheme. Then the mobility models are extended up to the inertial information taking into consideration the rotations of the nodes. A decentralized localization method is also proposed in the following in view of the working mechanism of large scale sensor networks. Finally, this thesis proposes a zoning localization method aiming at determining the zones in which the nodes reside. The proposed method addresses the zoning problem by using both the belief functions theory and the interval analysis

This thesis is divided in four chapters. The two first chapters introduce a parametric quantile-based estimation method of univariate heavy-tailed distributions and elliptical distributions, respectively. If one is interested in estimating the tail index without imposing a parametric form for the entire distribution function, but only on the tail behaviour, we propose a multivariate Hill estimator for elliptical distributions in chapter three. In the first three chapters we assume an independent and identically distributed setting, and so as a first step to a dependent setting, using quantiles, we prove in the last chapter the asymptotic normality of marginal sample quantiles for stationary processes under the S-mixing condition.<p><p><p>The first chapter introduces a quantile- and simulation-based estimation method, which we call the Method of Simulated Quantiles, or simply MSQ. Since it is based on quantiles, it is a moment-free approach. And since it is based on simulations, we do not need closed form expressions of any function that represents the probability law of the process. Thus, it is useful in case the probability density functions has no closed form or/and moments do not exist. It is based on a vector of functions of quantiles. The principle consists in matching functions of theoretical quantiles, which depend on the parameters of the assumed probability law, with those of empirical quantiles, which depend on the data. Since the theoretical functions of quantiles may not have a closed form expression, we rely on simulations.<p><p><p>The second chapter deals with the estimation of the parameters of elliptical distributions by means of a multivariate extension of MSQ. In this chapter we propose inference for vast dimensional elliptical distributions. Estimation is based on quantiles, which always exist regardless of the thickness of the tails, and testing is based on the geometry of the elliptical family. The multivariate extension of MSQ faces the difficulty of constructing a function of quantiles that is informative about the covariation parameters. We show that the interquartile range of a projection of pairwise random variables onto the 45 degree line is very informative about the covariation.<p><p><p>The third chapter consists in constructing a multivariate tail index estimator. In the univariate case, the most popular estimator for the tail exponent is the Hill estimator introduced by Bruce Hill in 1975. The aim of this chapter is to propose an estimator of the tail index in a multivariate context; more precisely, in the case of regularly varying elliptical distributions. Since, for univariate random variables, our estimator boils down to the Hill estimator, we name it after Bruce Hill. Our estimator is based on the distance between an elliptical probability contour and the exceedance observations. <p><p><p>Finally, the fourth chapter investigates the asymptotic behaviour of the marginal sample quantiles for p-dimensional stationary processes and we obtain the asymptotic normality of the empirical quantile vector. We assume that the processes are S-mixing, a recently introduced and widely applicable notion of dependence. A remarkable property of S-mixing is the fact that it doesn't require any higher order moment assumptions to be verified. Since we are interested in quantiles and processes that are probably heavy-tailed, this is of particular interest.<p> / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

Stochastic combinatorial optimization problems are combinatorial optimization problems where part of the problem data are probabilistic. The focus of this thesis is on stochastic routing problems, a class of stochastic combinatorial optimization problems that arise in distribution management. Stochastic routing problems involve finding the best solution to distribute goods across a logistic network. In the problems we tackle, we consider a setting in which the cost of a solution is described by a random variable; the goal is to find the solution that minimizes the expected cost. Solving such stochastic routing problems is a challenging task because of two main factors. First, the number of possible solutions grows exponentially with the instance size. Second, computing the expected cost of a solution is computationally very expensive. <p><br><p>To tackle stochastic routing problems, stochastic local search algorithms such as iterative improvement algorithms and metaheuristics are quite promising because they offer effective strategies to tackle the combinatorial nature of these problems. However, a crucial factor that determines the success of these algorithms in stochastic settings is the trade-off between the computation time needed to search for high quality solutions in a large search space and the computation time spent in computing the expected cost of solutions obtained during the search. <p><br><p>To compute the expected cost of solutions in stochastic routing problems, two classes of approaches have been proposed in the literature: analytical computation and empirical estimation. The former exactly computes the expected cost using closed-form expressions; the latter estimates the expected cost through Monte Carlo simulation.<p><br><p>Many previously proposed metaheuristics for stochastic routing problems use the analytical computation approach. However, in a large number of practical stochastic routing problems, due to the presence of complex constraints, the use of the analytical computation approach is difficult, time consuming or even impossible. Even for the prototypical stochastic routing problems that we consider in this thesis, the adoption of the analytical computation approach is computationally expensive. Notwithstanding the fact that the empirical estimation approach can address the issues posed by the analytical computation approach, its adoption in metaheuristics to tackle stochastic routing problems has never been thoroughly investigated. <p><br><p>In this thesis, we study two classical stochastic routing problems: the probabilistic traveling salesman problem (PTSP) and the vehicle routing problem with stochastic demands and customers (VRPSDC). The goal of the thesis is to design, implement, and analyze effective metaheuristics that use the empirical estimation approach to tackle these two problems. The main results of this thesis are: <p>1) The empirical estimation approach is a viable alternative to the widely-adopted analytical computation approach for the PTSP and the VRPSDC; <p>2) A principled adoption of the empirical estimation approach in metaheuristics results in high performing algorithms for tackling the PTSP and the VRPSDC. The estimation-based metaheuristics developed in this thesis for these two problems define the new state-of-the-art. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished