<div><p>This paper presents a novel approach to deal with nonlinear filtering by augmenting an Unscented Kalman Filter (UKF) with an Optimal quantization algorithm, named OQ-UKF. The Unscented Kalman Filter uses a sigma-point based method to approximate the distribution of an unknown random variable onto which is applied a nonlinear transformation, providing a cloud of evolving points. However, the generation of these socalled sigma-points is done by a deterministic algorithm which needs tuning in order to accurately capture the distribution of the estimate. This tuning is often problem-dependent due to nonlinearities and sometimes not optimal. We propose to fuse an UKF...

Le système de réception de signaux Global Navigation Satellite System (GNSS) présente une vulnérabilité face au phénomène de multitrajet. Événement fréquent en milieu urbain, il peut mener à une forte détérioration des performances de positionnement du récepteur. Malgré le développement de nombreuses techniques à des fins de détection ou de réduction de ses effets comme le Fast Iterative Maximum-Likelihood Algorithm (FIMLA) ou les filtres particulaires, la diminution de l'impact du multitrajet au sein d'un récepteur GNSS reste un objectif difficile à atteindre. Dans cette thèse, nous nous proposons d'appliquer un algorithme de Machine Learning pour éliminer la composante...

Air transportation network has been developed and optimised for years in order to provide passengers with a high level of service. However, the growing increase in environmental awareness and airport congestion make trains a relevant alternative to replace short-haul flights. If trains have to complement flights, collaboration between both operators will be required to maintain attractiveness and limit passengers' discomfort during transfers between modes. In this thesis, we propose synchronising air and rail timetables to enhance the quality of air-rail transfers for passengers at hub airports. As the transfer demand between rail and air is not publicly available, we...

The use of machine learning (ML) in airborne safety-critical systems requires new methods for certification, as the current standards and practices were defined and refined over decades with classical programming in mind and do not support this new development paradigm. This article provides an overview of the main challenges to the demonstration of compliance with regulation requirements raised by the use of ML, and focuses on one particular case where the formal verification may become mandatory in future regulations, which is the verification of (partial) monotony properties. For this case, we propose a method to evaluate the monotony property using Mixed Integer...

A crucial step in inbred plant breeding is the choice of mating design to derive high-performing inbred varieties while also maintaining a competitive breeding population to secure sufficient genetic gain in future generations. In practice, the mating design usually relies on crosses involving the best parental inbred lines to ensure high mean progeny performance. This excludes crosses involving lower performing but more complementary parents in terms of favorable alleles. We predicted the ability of crosses to produce putative outstanding progenies (high mean and high variance progeny distribution) using genomic prediction models. This study compared the benefits and...

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This paper proposes a method for computing an airliner trajectory in its cruise phase, minimizing environmental impact. In particular, the problem of contrails is addressed. The proposed method is based on an algorithm derived from robotics, the Fast Marching Tree algorithm. After having been tested on Unmanned Aerial Vehicles (UAVs) and on computing trajectories in a wind field for commercial aircraft, it is now adapted to the case of contrails. Several modifications and additions are made to enable it to deal with soft obstacles that evolve over time, as well as the operational constraints associated with the cruise phase. The method is thus able to propose flight level...

This study aims at modeling and investigating the impact of realistic turbulence inhomogeneity on radiowave propagation by utilizing large eddy simulations (LES). An up-to-date version of the X-LES method for generating realistic turbulent phase screens is first introduced. It combines atmospheric simulations with the classical Tatarski statistical modeling. This method naturally incorporates vertical turbulence inhomogeneity into phase screens at scales resolved by LES. It is then extended to sub-grid scales by weighting statistically generated phase variations with the vertical profile of the turbulent structure constant extracted from atmospheric data. This method is...

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