There is significant uncertainty in our knowledge of the Martian atmosphere and the aerodynamics of the Mars entry, descent, and landing (EDL) systems. These uncertainties result in conservatism in the design of the EDL vehicles leading to higher system masses and a broad range of performance predictions. Data from flight instrumentation onboard Mars EDL systems can be used to quantify these uncertainties, but the existing dataset is sparse and many parameters of interest have not been previously observable. Many past EDL reconstructions neither utilize statistical information about the uncertainty of the measured data nor quantify the uncertainty of the estimated parameters. Statistical estimation methods can blend together disparate data types to improve the reconstruction of parameters of interest for the vehicle. For example, integrating data obtained from aeroshell-mounted pressure transducers, inertial measurement unit, and radar altimeter can improve the estimates of the trajectory, atmospheric profile, and aerodynamic coefficients, while also quantifying the uncertainty in these estimates. These same statistical methods can be leveraged to improve current engineering models in order to reduce conservatism in future EDL vehicle design. The work in this thesis presents a comprehensive methodology for parameter reconstruction and uncertainty quantification while blending dissimilar Mars EDL datasets. Statistical estimation methods applied include the Extended Kalman Filter, Unscented Kalman Filter, and Adaptive Filter. The estimators are applied in a manner in which the observability of the parameters of interest is maximized while using the sparse, disparate EDL dataset. The methodology is validated with simulated data and then applied to estimate the EDL performance of the 2012 Mars Science Laboratory. The reconstruction methodology is also utilized as a tool for improving vehicle design and reducing design conservatism. A novel method of optimizing the design of future EDL atmospheric data systems is presented by leveraging the reconstruction methodology. The methodology identifies important design trends and the point of diminishing returns of atmospheric data sensors that are critical in improving the reconstruction performance for future EDL vehicles. The impact of the estimation methodology on aerodynamic and atmospheric engineering models is also studied and suggestions are made for future EDL instrumentation.

La synchronisation temporelle est la première opération effectuée par le démodulateur. Elle permet d'assurer que les échantillons transmis aux processus de démodulation puissent réaliser un taux d'erreurs binaires le plus faible.Dans cette thèse, nous proposons l'étude d'algorithmes innovants de synchronisation temporelle à haute performance.D'abord, nous avons proposé des algorithmes exploitant l'information souple du décodeur en plus du signal reçu afin d'améliorer l'estimation aveugle d'un retard temporel supposé constant sur la durée d'observation.Ensuite, nous avons proposé un algorithme original basé sur la synchronisation par lissage à faible complexité.Cette étape a consisté à proposer une technique opérant dans un contexte hors ligne, permettant l'estimation d'un retard aléatoire variable dans le temps via les boucles d'aller-retour sur plusieurs itération. Les performances d'un tel estimateur dépassent celles des algorithmes traditionnels.Afin d'évaluer la pertinence de tous les estimateurs proposés, pour des retards déterministe et aléatoire, nous avons évalué et comparé leurs performances à des bornes de Cramèr-Rao que nous avons développées pour ce cadre. Enfin, nous avons évalué les algorithmes proposés sur des signaux WBAN. / Time synchronization is the first function performed by the demodulator. It ensures that the samples transmitted to the demodulation processes allow to achieve the lowest bit error rate.In this thesis we propose the study of innovative algorithms for high performance time synchronization.First, we propose algorithms exploiting the soft information from the decoder in addition to the received signal to improve the blind estimation of the time delay. Next, we develop an original algorithm based on low complexity smoothing synchronization techniques. This step consisted in proposing a technique operating in an off-line context, making it possible to estimate a random delay that varies over time on several iterations via Forward- Backward loops. The performance of such estimators exceeds that of traditional algorithms. In order to evaluate the relevance of all the proposed estimators, for deterministic and random delays, we evaluated and compared their performance to Cramer-Rao bounds that we developed within these frameworks. We, finally, evaluated the proposed algorithms on WBAN signals.

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Oceanic microwave remote sensing provides the data necessary for the estimation of significant geophysical parameters such as the near-surface vector wind. To obtain accurate estimates, a precise understanding of the measurements is critical. This work clarifies and quantifies specific uncertainties in the scattered power measured by an active radar instrument. While there are many sources of uncertainty in remote sensing measurements, this work concentrates on three significant, yet largely unstudied effects. With a theoretical derivation of the backscatter from an ocean-like surface, results from this dissertation demonstrate that the backscatter decays with surface roughness with two distinct modes of behavior, affected by the size of the footprint. A technique is developed and scatterometer data analyzed to quantify the variability of spaceborne backscatter measurements for given wind conditions; the impact on wind retrieval is described in terms of bias and the Cramer-Rao lower bound. The probability density function of modified periodogram averages (a spectral estimation technique) is derived in generality and for the specific case of power estimates made by the NASA scatterometer. The impact on wind retrieval is quantified.

This project is associated with statistical methods to find the unknown parameters of a model. It is the statistical investigation of the algorithm with respect to accuracy (the Cramer-Rao bound and L-curve technique) and optimization of the algorithmic parameters. This project aims to estimate the true temperature (final temperature) of a certain liquid in a container by using initial measurements (readings) from a temperature probe with a known time constant. Basically, the final temperature of the liquid was estimated, before the probe reached its final reading. The probe obeys a simple first-order differential equation model. Based on the model of the probe and the measurement data the estimate was calculated of the ’true’ temperature in the container by using a maximum likelihood approach to parameter estimation.  The initial temperature was also investigated. Modelling, analysis, calculations, and simulations of this problem were explored.

Sensor network performance is governed by the physical placement of sensors and their geometric relationship to the events they measure. To illustrate this, the entirety of this thesis covers the following interconnected subjects: 1) graphical analysis of the variance of the estimation error caused by physical characteristics of an acoustic target source and its geometric location relative to sensor arrays, 2) event/anomaly detection method for time aggregated point sensor data using a parametric Poisson distribution data model, 3) a sensor reduction or placement technique using Bellman optimal estimates of target agent dynamics and probabilistic training data (Goode, Chin, & Roan, 2011), and 4) transforming event monitoring point sensor data into event detection and classification of the direction of travel using a contextual, joint probability, causal relationship, sliding window, and geospatial intelligence (GEOINT) method. / Master of Science

L'un des enjeux majeurs pour les systèmes d'imagerie par diffraction acoustique et micro-onde, est l'amélioration des performances obtenues au moment de la reconstruction des objets étudiés. Cette amélioration peut s'effectuer par la recherche d'algorithmes d'imagerie plus performants d'une part et par la recherche d'une meilleure configuration de mesures d'autre part. La première approche (recherche d'algorithmes) permet d'améliorer le processus d'extraction de l'information présente dans un échantillon de mesures donné. Néanmoins, la qualité des résultats d'imagerie reste limitée par la quantité d'information initialement disponible. La seconde approche consiste à choisir la configuration de mesures de manière à augmenter la quantité d'information disponible dans les données. Pour cette approche, il est nécessaire de quantifier la quantité d'information dans les données. En théorie de l'estimation, ceci équivaut à quantifier la performance du système. Dans cette thèse, nous utilisons la Borne de Cramer Rao comme mesure de performance, car elle permet d'analyser la performance des systèmes de mesures sans être influencé par le choix de la méthode d'inversion utilisée. Deux analyses sont proposées dans ce manuscrit. La première consiste en l'évaluation de l'influence des facteurs expérimentaux sur la performance d'inversion. Cette analyse a été effectuée pour différents objets le tout sous une hypothèse de configuration bidimensionnelle. La seconde analyse consiste à comparer les performances de l'estimateur obtenu avec l'approximation de Born aux valeurs de la borne de Cramer Rao (BCR); l'objectif étant d'illustrer d'autres applications possibles de la BCR. / Improving the performance of diffraction based imaging systems constitutes a major issue in both acoustic and electromagnetic scattering. To solve this problem, two main approaches can be explored. The first one consists in improving the inversion algorithms used in diffraction based imaging. However, while this approach generally leads to a significant improvement of the performance of the imaging system, it remains limited by the initial amount of information available within the measurements. The second one consists in improving the measurement system in order to maximize the amount of information within the experimental data. This approach does require a quantitative mean of measuring the amount of information available. In estimation problems, the {appraisal of the} performance of the system is often used for that purpose. In this Ph.D. thesis, we use the Cramer Rao bound to assess the performance of the imaging system. In fact, this quantity has the advantage of providing an assessment which is independent from the inversion algorithm used. Two main analysis are discussed in this thesis. The first analysis explores the influence on the system's performance, of several experimental conditions such as the antennas positions, the radiation pattern of the source, the properties of the background medium, etc. Two classes of objects are considered: 2D homogeneous circular cylindrical objects and 2D cylindrical objects with defect. The second analysis studies the performance of an estimator based on Born approximation with the Cramer Rao Bound as reference. The aim of this second analysis is to showcase other possible applications for the Cramer Rao Bound.

Cognitive radio is a recent novel approach for the realization of intelligent and sophisticated wireless systems. Although the research and development on cognitive radio is still in the stage of infancy, there are significant interests and efforts towards realization of cognitive radio. Cognitive radio systems are envisioned to support context awareness and related systems. The context can be spectrum, environment, location, waveform, power and other radio resources. Significant amount of the studies related to cognitive radio in the literature focuses on the spectrum awareness since it is one of the most crucial features of cognitive radio systems. However, the rest of the features of cognitive radio such as location and environment awareness have not been investigated thoroughly. For instance, location aware systems are widespread and the demand for more advanced ones are growing. Therefore, the main objective of this dissertation is to develop an underlying location awareness architecture for cognitive radio systems, which is described as location awareness engine, in order to support goal driven and autonomous location aware systems. A cognitive radio conceptual model with location awareness engine and cycle is developed by inspiring from the location awareness features of human being and bat echolocation systems. Additionally, the functionalities of the engine are identified and presented. Upon providing the functionalities of location awareness engine, the focus is given to the development of cognitive positioning systems. Furthermore, range accuracy adaptation, which is a cognitive behavior of bats, is developed for cognitive positioning systems. In what follows, two main approaches are investigated in order to improve the performance of range accuracy adaptation method. The first approach is based on idea of improving the spectrum availability through hybrid underlay and overlay dynamic spectrum access method. On the other hand, the second approach emphasizes on spectrum utilization, where we study performance of range accuracy adaptation from both theoretical and practical perspectives considering whole spectrum utilization approach. Furthermore, we introduced a new spectrum utilization technique that is referred as dispersed spectrum utilization. The performance analysis of dispersed spectrum utilization approach is studied considering time delay estimation problem in cognitive positioning systems. Afterward, the performance of whole and dispersed spectrum utilization approaches are compared in the context of cognitive positioning systems. Finally, some representative advanced location aware systems for cognitive radio networks are presented in order to demonstrate some potential applications of the proposed location awareness engine in cognitive radio systems.

L'anémométrie laser à effet Doppler (ALD), dont l'utilisation est très répandue en mécanique des fluides, peut aussi être utilisée pour la mesure de vitesse particulaire acoustique. La nature vectorielle de cette grandeur permet de caractériser les champs à structure complexe ce qui autorise, par exemple, l'étude de phénomènes acoustiques au voisinage de parois vibrantes ou de discontinuités géométriques. L'ALD offre, pour ce type de problème, le double avantage de présenter une bonne résolution spatiale de la mesure et de ne perturber que faiblement le champ à explorer.<br />Le Laboratoire d'Acoustique de l'Université du Maine est équipé d'un banc de mesure conçu pour la mesure de vitesse acoustique. Depuis près de huit ans, les recherches se sont orientées vers la validation expérimentale de cette technique ainsi que vers l'élaboration de méthodes de traitement des signaux propres à extraire la vitesse particulaire dans un contexte de traitement différé et avec l'hypothèse d'un signal Doppler d'amplitude constante. Cependant, pour que l'ALD soit exploitable efficacement il est indispensable de concevoir un système de mesure, simple d'utilisation, permettant d'accéder rapidement à la vitesse acoustique et prenant en compte la nature variable de l'amplitude du signal Doppler.<br />L'objectif de cette étude est donc de proposer des méthodes de traitement du signal adaptées à l'estimation de la vitesse particulaire acoustique, dans le cas d'une excitation sinusoïdale, et de définir une architecture matérielle et logicielle indispensable à cette mesure. Celle-ci s'opère à partir des composantes en phase et en quadrature du signal Doppler ramenées en bande de base à l'aide d'un dispositif de démodulation analogique spécialement développé pour cette application. Trois estimateurs sont alors proposés : le premier est basé sur le calcul de la dérivée de la phase du signal Doppler. Sa principale vocation consiste à initialiser une deuxième méthode d'estimation basée sur la recherche du maximum de vraisemblance. Le filtrage de Kalman étendu constitue la troisième méthode explorée. Enfin, les formes analytiques, approchées mais précises, des bornes de Cramer-Rao montrent l'influence des paramètres du problème sur la qualité des estimations. Par la suite, des simulations statistiques de Monte-Carlo permettent d'évaluer la qualité des trois méthodes. Une estimation sommaire des complexités algorithmiques des estimateurs complète également cette phase d'évaluation.<br />Enfin, la validation expérimentale des techniques d'estimation s'articule autour de deux expériences. Dans un premier temps, la mesure de la vitesse de déplacement sinusoïdale d'une pointe d'aiguille montée dans un pot vibrant permet de confronter les résultats obtenus par ALD à ceux que donne un vibromètre laser. Dans un second temps, la mesure de vitesses particulaires acoustiques rayonnées par un haut-parleur électrodynamique, est proposée. Les valeurs issues de la mesure en champ libre par ALD sont alors comparées à celles des vitesses de référence obtenues à l'aide de la méthode du doublet microphonique.

This thesis deals with the problem of estimating the velocity of a mobile station (MS)in a mobile communication system using the instantaneous frequency (IF) of the received signal at the MS antenna. This estimate is essential for satisfactory handover performance, effective dynamic channel assignment, and optimisation of adaptive multiple access wireless receivers. Conventional methods for estimating the MS velocity are based either on the statistics of the envelope or quadrature components of the received signal. In chapter 4 of the thesis, we show that their performance deteriorates in the presence of shadowing. Other velocity estimators have also been proposed which require prior estimation of the channel or the average received power. These are generally difficult to obtain due to the non-stationary nature of the received signal. An appropriate window which depends on the unknown MS velocity must first be applied in order to accurately estimate the required quantities. Using the statistics of the IF of the received signal at the MS antenna given in chapter 3, new velocity estimators are proposed in chapter 4 of this thesis. The proposed estimators are based on the moments, zero-crossing rate, and covariance of the received IF. Since the IF of the received signal is not affected by any amplitude distortion, the proposed IF-based estimators are robust to shadowing and propagation path-loss. The estimators for the MS velocity in a macro- and micro-cellular system are presented separately. A macro-cell system can be considered as a special case of a micro-cell in which there is no line-of-sight component at the receiver antenna. It follows that those estimators which are derived for micro-cells can be used in a macro-cell as well. In chapter 4, we analyse the performance of the proposed velocity estimators in the presence of additive noise, non-isotropic scattering, and shadowing. We also prove analytically that the proposed velocity estimators outperform the existing methods in the presence of shadowing and additive noise. The proposed IF-based estimators need prior estimation of both the IF of the received signal and Ricean K-factor. The IF estimation in a typical wireless environment, can be considered as a special case of a general problem of IF estimation in the presence of multiplicative and additive noise. In chapter 5, we show that current time-frequency approaches to this problem which are based on the peak of a time-frequency distribution (TFD) of the signal, fail because of the special shape of the power spectral density of the multiplicative noise in a wireless environment. To overcome this drawback, the use of the first-order moment of a TFD is studied in chapter 5. Theoretical analysis and simulations show that the IF estimator based on the first-order moment of a TFD exhibits negligible bias when the signal-to-additive noise ratio is more than 10 dB. The Ricean K-factor is not only necessary for velocity estimation in micro-cells, but also is a measure of the severity of fading and a good indicator of the channel quality. Two new methods for estimating the Ricean K-factor based on the first two moments of the envelope of the received signal, are proposed in chapter 6. Performance analysis presented in chapter 6, prove that the proposed K estimators are robust to non-isotropic scattering. Theoretical analysis and simulations which are presented in chapters 4 and 7 of this thesis, prove that the proposed velocity and K estimators outperform existing estimators in the presence of shadowing and additive noise.