L’objectif principal du travail de recherche présenté dans ce mémoire est l’amélioration de la sécurité et les performances du vol des mini drones soumis à des perturbations atmosphériques. Pour ce faire, un couplage entre un estimateur ensembliste à erreurs bornées et une stratégie de guidage pilotage est mise en œuvre. L’estimateur ensembliste a été utilisé pour restituer l’état du modèle dynamique du drone en présence de perturbations et de bruits de mesure supposés bornés. L’utilisation de ces techniques avait pour objet tout d’abord de détecter l’occurrence d’une perturbation atmosphérique par estimation de l’état du drone puis d’estimer l’amplitude et la direction du vent agissant sur le véhicule. Des expérimentations dans le générateur de rafale B20 à Lille ont été ainsi présentées afin de valider ces approches et d’évaluer leurs performances. La stratégie de guidage pilotage développée favorise le déplacement du véhicule dans une direction qui tient compte de l’évolution de la perturbation atmosphérique et du prochain point de passage désigné au véhicule. Cette loi de guidage est basée sue la loi de guidage par navigation proportionnelle et a été adaptée pour tenir compte des perturbations dans le déplacement du véhicule. Les résultats obtenus montrent qu’il est possible d’améliorer la sécurité du vol des mini-drones en présence de perturbations atmosphériques transversales, en modifiant en ligne la trajectoire. / The principal objective of this thesis is to enhance the safety of flight for small UAVs in presence of atmospheric perturbation. The approach suggested here consists in coupling a bounded–error estimation method with a new guidance strategy. The bounded error estimation has been used to estimate the states of the dynamical systems corrupted by perturbations and measurement noises, assumed to remain bounded. The method has been first used to detect the occurrence of a wind gust and afterwards to characterize the amplitude and direction of the wind acting on the vehicle Experiments in the B20 gust generator are also presented to validate these approaches and evaluate their performance. The developed guidance strategy provides the vehicle with a direction that takes into account the atmospheric perturbation and the next waypoint position. The guidance law is designed by using proportional navigation guidance that has been adapted to take the perturbations into account. The results presented in this thesis show that it is possible to improve the flight safety in a perturbed environement using the combination of the two methods.

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Yiu Choi Fan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 69-71). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Estimation with Known Misclassification Probabilities --- p.7 / Chapter 2.1 --- Model --- p.7 / Chapter 2.2 --- Maximum Likelihood Estimation --- p.9 / Chapter 2.3 --- Standard Errors of the Parameter Estimates --- p.12 / Chapter 3 --- Numerical Examples (I) --- p.13 / Chapter 3.1 --- Analysis of Real Data --- p.13 / Chapter 3.2 --- Analysis of Artificial Data --- p.16 / Chapter 4 --- Simulation Study (I) --- p.19 / Chapter 4.1 --- Simulation Algorithm --- p.19 / Chapter 4.2 --- Simulation Design --- p.20 / Chapter 4.3 --- Reported Statistics --- p.21 / Chapter 4.4 --- Conclusions of Simulation Results --- p.22 / Chapter 5 --- Estimation by Double Sampling Scheme --- p.24 / Chapter 5.1 --- Introduction of Double Sampling Scheme --- p.24 / Chapter 5.2 --- Model --- p.25 / Chapter 5.3 --- Minimum Chi-square Estimation --- p.26 / Chapter 5.4 --- Statistical Properties of the Parameter Estimates --- p.28 / Chapter 6 --- Numerical Examples (II) --- p.30 / Chapter 6.1 --- "Analysis of Real Data, (2x2 Table)" --- p.30 / Chapter 6.2 --- Analysis of Artificial Data (3x3 Table) --- p.32 / Chapter 7 --- Simulation Study (II) --- p.34 / Chapter 7.1 --- Simulation Algorithm --- p.34 / Chapter 7.2 --- Simulation Design --- p.35 / Chapter 7.3 --- Reported Statistics --- p.37 / Chapter 7.4 --- Conclusions of Simulation Results --- p.38 / Chapter 8 --- Conclusions --- p.39 / Appendices --- p.42 / Chapter A.1 --- The proof of the expression for P(Zj = Ehk) --- p.42 / Chapter A.2 --- The proof of puv and whk{uv) --- p.44 / Chapter A.3 --- The proof of the covariance matrix Q --- p.47 / Chapter A.4 --- The proof of the matrix Σ --- p.52 / Tables A1-A9 --- p.54 / Tables B1-B6 --- p.63 / Bibliography --- p.69

Kong, Cheuk Kwan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 42-44). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Modelling Piecewise AR model --- p.4 / Chapter 2.1 --- Background --- p.4 / Chapter 2.2 --- Introduction to Auto-FARM --- p.5 / Chapter 2.3 --- Minimum Description Length --- p.6 / Chapter 2.4 --- Genetic Algorithm --- p.9 / Chapter 2.5 --- Reproduction Rules --- p.10 / Chapter 3 --- Bayesian-SCAD Approach --- p.14 / Chapter 3.1 --- Estimation via Penalty Function --- p.15 / Chapter 3.2 --- Introduction to SCAD --- p.17 / Chapter 3.3 --- Local Quadratic Approximation of SCAD --- p.20 / Chapter 3.4 --- Bayesian Formulation and GA Implementation --- p.22 / Chapter 4 --- Simulation Study --- p.25 / Chapter 4.1 --- Piecewise AR Process from Davis et al. (2006) --- p.25 / Chapter 4.2 --- Piecewise Seasonal AR Process --- p.29 / Chapter 5 --- Real Data Analysis --- p.33 / Chapter 5.1 --- Description and Source of Data --- p.33 / Chapter 5.2 --- Model Fitting --- p.36 / Chapter 5.3 --- Prediction Results --- p.39 / Chapter 6 --- Conclusion --- p.40 / Bibliography --- p.42

Lin Jianzhong. / "May 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 106-118). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

by Li Xia. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (p. 129-134). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

For discretization method, which provides an effective way to handle the problem, we focus on constructing a discretized version of continuous failure times. The major observation is that discrete failure times with finite values will attain their exact values although only ranks are given, as long as sample size is large enough. The score function can be asymptotically approximated by a sum of independent random variables. Consistency, asymptotic normality and efficiency of estimator can be obtained by using standard results for estimating equations, given some milder and more feasible conditions than those of martingale method. / In this thesis, some finite sample properties of marginal likelihood will be established. We prove under some regular conditions the score function of the marginal likelihood is a martingale, and prove the marginal likelihood satisfies some properties enjoyed by the standard likelihood method, although only use the relative ranks to make inference of the parameter instead of the full information. / In this thesis, the author studies some asymptotic properties of the marginal maximum likelihood estimate (marginal MLE) for general transformation models. The general transformation model is an important class of models for survival times and is nontrivially more general model than the linear transformation model (Gu, Sun and Zuo, 2005). By using marginal likelihood, we obtain estimator of regression parameter which does not depend on its baseline survival function, a property enjoyed by the Cox regression model. The major obstacle for the general transformation models is the resulting estimation function is complicated and usually has no closed analytic expression. Gu etc. (2005) proposed Markov chain Monte Carlo (MCMC) stochastic approximation algorithm to solve the marginal MLE. / Moreover, a discussion is given for the question of asymptotic properties for the proposed marginal MLE based on two different methods: martingale method and discretization method. For martingale method, emphasis is given to the role of martingale limit theory and results presented are primarily theoretical. / We also demonstrate some important transformation models do satisfy our conditions and thus show their consistency, asymptotic normality and efficiency for the first time. / Huang Bin. / "Dec 2005." / Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6484. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 63-69). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

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Wong, Ka Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 46-49). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.4 / Chapter 2.1 --- Notation and Definitions --- p.4 / Chapter 2.2 --- Influence Function of β --- p.5 / Chapter 3 --- Methodology --- p.7 / Chapter 3.1 --- Robust Estimating Equations --- p.7 / Chapter 3.2 --- A General Algorithm for Robust GAM Estimation --- p.9 / Chapter 4 --- Asymptotic Equivalence --- p.12 / Chapter 5 --- Smoothing Parameter Selection --- p.16 / Chapter 5.1 --- Robust Cross-Validation --- p.17 / Chapter 5.2 --- Robust Information Criteria --- p.17 / Chapter 6 --- Multiple Covariates --- p.19 / Chapter 7 --- Simulation Study --- p.21 / Chapter 8 --- Real Data Examples --- p.26 / Chapter 8.1 --- Air Pollution Data --- p.26 / Chapter 8.2 --- Bronchitis Data --- p.28 / Chapter 9 --- Concluding Remarks --- p.31 / Chapter A --- Auxiliary Lemmas and Proofs --- p.32 / Chapter B --- Fisher Consistency Correction --- p.42 / Chapter B.1 --- Poisson distribution --- p.42 / Chapter B.2 --- Bernoulli distribution --- p.43 / Chapter C --- Derivation of (5.2) --- p.44 / Bibliography --- p.46

Tam, Hiu Wah. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 41). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Estimation with Known Misclassification Probabilities --- p.5 / Chapter 2.1 --- Model --- p.5 / Chapter 2.2 --- Maximum Likelihood Estimation --- p.7 / Chapter 2.3 --- Examples --- p.9 / Chapter 2.3.1 --- Example 1: A Real data set analysis --- p.9 / Chapter 2.3.2 --- Example 2: An Artificial Data for 3x3 Table --- p.11 / Chapter 3 --- Estimation by Double Sampling --- p.12 / Chapter 3.1 --- Estimation --- p.13 / Chapter 3.2 --- Example --- p.14 / Chapter 3.2.1 --- Example 3: An Artificial Data Example for 3x3 Table --- p.14 / Chapter 4 --- Simulation --- p.15 / Chapter 5 --- Conclusion --- p.17 / Table --- p.19 / Appendix --- p.27 / Bibliography --- p.41