Hyperspectral images present some specific characteristics that should be used by an efficient compressionsystem. This thesis focuses on the definition and the optimization of a full wavelet compression system for hyperspectral images. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Zerotree based compression algorithms are among the best for image compression. Therefore, in this work, efficient compression methods based on zerotree coding (EZW, SPIHT) are adapted on a near-optimal wavelet decomposition for hyperspectral images. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images. End users of hyperspectral images are often interested only in some specific features of the image (resolution, location) which depend on the application. A further adaptation of the proposed hyperspectral image compression algorithm is presented to allow random access to some part of the image, whether spatial or spectral. Resolution scalability is also available, enabling the decoding of different resolution images from the compressed bitstream of the hyperspectral data while reading a minimum amount of bits from the coded data. Final spatial and spectral resolutions are chosen independantly. Finally, any lossless compression method cannot be characterized without the definition of a distortion measure. Therefore, a group of five quality criteria presenting a good complementarity is defined. The purpose is to make sure the compression algorithm does not impact significantly the data quality. A new method using these five criteria shows a good ability to discriminate between different degradations. Application of this method to the newly defined algorithm shows that the degradation remains low for compression rate around 1.0 bit per pixel per band.

Hyperspectral images present some specific characteristics that should be used by an efficient compressionsystem. This thesis focuses on the definition and the optimization of a full wavelet compression system for hyperspectral images. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Zerotree based compression algorithms are among the best for image compression. Therefore, in this work, efficient compression methods based on zerotree coding (EZW, SPIHT) are adapted on a near-optimal wavelet decomposition for hyperspectral images. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images. End users of hyperspectral images are often interested only in some specific features of the image (resolution, location) which depend on the application. A further adaptation of the proposed hyperspectral image compression algorithm is presented to allow random access to some part of the image, whether spatial or spectral. Resolution scalability is also available, enabling the decoding of different resolution images from the compressed bitstream of the hyperspectral data while reading a minimum amount of bits from the coded data. Final spatial and spectral resolutions are chosen independantly. Finally, any lossless compression method cannot be characterized without the definition of a distortion measure. Therefore, a group of five quality criteria presenting a good complementarity is defined. The purpose is to make sure the compression algorithm does not impact significantly the data quality. A new method using these five criteria shows a good ability to discriminate between different degradations. Application of this method to the newly defined algorithm shows that the degradation remains low for compression rate around 1.0 bit per pixel per band.

Ionosphere is a dispersive medium that can strongly affect GPS and GALILEO signals. Ionospheric delay affecting the GPS and GALILEO pseudorange measurements is the larger source of ranging error, if left uncorrected. In addition, this perturbation is difficult to model and thus difficult to predict. A multi-frequency receiver can identify and correct errors induced by the ionosphere, as in the nominal case, two frequencies are sufficient to determine precisely the ionospheric delay. However, if affected by radio frequency interference, a receiver can lose one or more frequencies leading to the use of only one frequency to estimate ionospheric code delay. Therefore, it is felt by the authors as an important task to investigate techniques aimed at sustaining multi-frequency performance when a multiconstellation receiver installed in an aircraft is suddenly affected by radiofrequency interference, during critical phases of flight. The case of a loss of all but one frequency is studied in [Shau-Shiun Jan, 2003]. In this case, the usual code-carrier divergence technique is analyzed, consisting in computing the difference between the signal code and the carrier phase measurements. This difference is twice the ionospheric delay plus ambiguity plus errors, from which the ionospheric delay can be extracted. If a cycle slip occurs, the integer ambiguity appearing as a constant offset in the code-carrier difference causes this technique not to be valid. In the case of a single frequency receiver, a Kalman filter can be used to determine if a cycle slip occurs, introducing ambiguities of all satellites in view in the state vector as mentioned in [Lestarquit, 1995]. This Kalman filter can be initialized in the dual frequency mode, and left running when only one frequency is left. The aim of this paper is first to propose a method for single frequency ionospheric delay estimation after the loss of multiple frequency tracking, and also to analyse the performance of this method with regards to the civil aviation requirements. The proposed technique includes the detection of cycle slips.

This thesis is devoted to the definition and the evaluation of multiuser detection techniques to mitigate co-channel interference on the reverse link of multibeam satellite systems. These techniques can cope with lower C/I than classical systems: they can consequently allow more capacity efficient frequency reuse strategies. The considered access and waveforms are inspired by the DVB-RCS standard. We propose iterative interference cancellation algorithms adapted to the satellite context. They include estimation of beamforming coefficients and frequency offsets of received signals. These algorithms are first evaluated in terms of bit error rate and of channel estimation error on fictitious interference configurations. We show that they lead to very limited degradations (with respect to the single user case) on interference configurations characterized by very low C/I. We then consider evaluations on a multibeam coverage. Simulation results on a multibeam coverage designed on a Focal Array Fed Reflector antenna allow comparing the algorithms in a realistic context.

This paper studies a new spectral analysis strategy for detecting, characterizing and classifying spectral structures of an unknown stationary process. The spectral structures we consider are defined as sinusoidal waves, narrow band signals or noise peaks. A sum of an unknown number of these structures is embedded in an unknown colored noise. The proposed methodology provides a way to calculate a spectral identity card, which features each of these spectral structures, similarly to a real I.D. The processing is based on a local Bayesian hypothesis testing, which is defined in frequency and which takes account of the noise spectrum estimator. Thanks to a matching with the corresponding spectral window, each I.D. card permits the classification of the associated spectral structure into one of the following four classes: Pure Frequency, Narrow Band, Alarm and Noise. Each I.D. card is actually the result of the fusion of intermediate cards, obtained from complementary spectral analysis methods.

Within the frame of machinery maintenance, spectral analysis is a helpful tool. Therefore, an automatic spectral analysis tool, capable to identify each component of a measured signal would be of interest. This paper studies a new spectral analysis strategy for detecting, characterizing and classifying all spectral components of an unknown process. Indeed, any vibration signal can be considered as a mixture of components, a component being either a sinusoidal wave, or a narrow band one. We assume that a sum of an unknown number of these components is embedded in an unknown colored noise. The complete methodology we propose provides a way to feature each component in the spectral domain. The first idea is not to choose one specific spectral analysis method but, rather, to concatenate the results of complementary algorithms. For each one, the noise spectrum is estimated by a nonlinear filter and spectral component detection is managed with a local Bayesian hypothesis testing. This test is defined in frequency and takes account of the noise spectrum estimator. Thanks to a matching with the corresponding spectral window, each component detected is classified into one of the following four classes: Pure Frequency, Narrow Band, Alarm and Noise. The second main idea is then to propose a fusion of the classification results, leading to a complete description of each spectral component present in the signal. This spectral classification is particularly interesting within the context of condition monitoring. Examples are given on real vibratory signals and show the performance of the proposed automatic method, which is particularly well adapted to signals having a high number of components.

La quantité de données produite par les capteurs hyperspectraux nécessite un algorithme de compression efficace qui restè a définir. Les propriétés statistiquesparticulì eres devraient permettre d'obtenir des algorithmes de compression efficaces Etant données ses propriétés et sa faible complexité, la transformée en ondelettes est un candidat prometteur pour la décorrélation des images hyperspectrales. Ce papier propose une méthode pour trouver la décomposition en ondelettes optimale pour les images hyperspectrales et in-troduit la possibilité d'une décomposition non isotropique. La décomposition donnant le meilleur compromis débit-distortion est choisie. Cette décomposition donne de bien meilleures per-formances en terme de débit-distortion que la décomposition isotropique classique. L'inconvénient de cette décomposition optimale réside dans sa complexité importante. Une seconde décomposition, fixe cette fois, est définie et montre des perfor-mances quasi optimales tout en gardant une complexité faible.

Hyperspectral sensors have been of a growing interest over the past few decades for Earth observation as well as deep space exploration. However, the amount of data provided by such sensors requires an efficient compression system which is yet to be defined. It is hoped that the particular statistical properties of such images can be used to obtain very efficient compression algorithms. This paper proposes a method to find the most suitable wavelet decomposition for hyperspectral images and introduces the possibility of non isotropic decomposition. The decomposition is made by choosing the decomposition that provides an optimal rate-distortion trade-off. The obtained decomposition exhibits better performances in terms of rate-distortion curves compared to isotropic decomposition for high bitrates as well as for low bitrates.

Due to the continuous increase of airport traffic, there is the need to improve the safety of vehicle ground movements in the different airport areas and to improve also the efficiency of airport operations: several tens to hundreds of ground vehicles are sharing areas with the normal air traffic. The CLESTA project contributes to the solution of this problem by developing a new low-cost and modular platform with three components, namely, on-board system, communication network and ground system. The paper presents the platform architecture focussing into its innovative characteristics to provide, in an airport environment, the surveillance, control and guidance services for the airport main actors. This project led by M3 Systems, in partnership with TeSA and ENAC laboratories, and Intuilab company, aims at designing and deploying a positioning system for airport ground vehicles with service messages transmitted via a Wireless Network (Wifi, WiMax, ...) deployed outside in the airport area. In this context, technical objectives of the project consist in developing the software required for transmitting and processing of the service messages.

The European satellite navigation system GALILEO will provide radionavigation signals for a variety of applications. Safety Of Life users will get a safe navigation service through ranging signals carrying integrity information. The Galileo Integrity Baseline algorithm includes the transmission of three parameters allowing users to monitor their integrity level. These parameters are the Signal-In-Space Accuracy (SISA: prediction of the minimum standard deviation of a Gaussian distribution overbounding the Signal-In-Space error in the fault-free case), the Signal-In-Space Monitoring Accuracy (SISMA: minimum standard deviation of a Gaussian distribution overbounding the difference between Signal- In-Space error and its estimation by ground control stations) and the Integrity Flag, which accounts for satellite status (it can be set to 'OK', 'DON'T USE' or 'NOT MONITORED'). These parameters are part of the input of the user integrity algorithm, which computes user integrity risk at the alert limit and compares it to the Integrity Risk requirement corresponding to user's phase of flight. The work presented in this paper studies the influence of the algorithm used for computation of SISMA on user integrity and system availability. The algorithms used to compute SISMA are the reference Least-Squares and several robust methods, designed to reject wrong measurements and decrease ground system False Alarm rate (fault-free satellites flagged 'DON'T USE').