Zerotree based coders have shown a good ability to be successfully adapted to 3D image coding. This paper focuses on the adaptation of EZW for the compression of hyperspectral images with reduced complexity. The subordinate pass is removed so that the location of significant coefficients does not need to be kept in memory. To compensate the quality loss due to this removal, a signed binary digit representation is used to increase the efficiency of zerotrees. Contextual arithmetic coding with very limited contexts is also used. Finally, we show that this simplified version of 3D-EZW performs almost as well as the original one.

Zerotrees of wavelet coefficients have shown a good adaptability for the compression of three-dimensional images. EZW, the original algorithm using zerotree, shows good performance and was successfully adapted to 3D image compression. This paper focuses on the adaptation of EZW for the compression of hyperspectral images. The subordinate pass is suppressed to remove the necessity to keep the significant pixels in memory. To compensate the loss due to this removal, signed binary digit representations are used to increase the efficiency of zerotrees. Contextual arithmetic coding with very limited contexts is also used. Finally, we show that this simplified version of 3D-EZW performs almost as well as the original one.

Ionosphere is a dispersive medium that can strongly affect GPS and GALILEO signals. Ionospheric delay affecting the GPS and GALILEO single frequency pseudorange measurements is the largest source of ranging error. In addition, this perturbation is difficult to model and thus difficult to predict. Nominal dual frequency measurements provide a good estimation of ionospheric delay. In addition, the combination of GPS and GALILEO navigation signals at the receiver level is expected to provide important improvements for civil aviation. It could, potentially with augmentations, provide better accuracy and availability of ionospheric correction measurements. Indeed, GPS users will be able to combine GPS L1 and L5 frequencies, and future GALILEO signals will bring their contribution as some of them will be transmitted at the same frequencies as the GPS signals. 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 the performance of techniques trying to sustain multi-frequency performance when a multi-constellation receiver installed in an aircraft loses dual frequency capability, during critical phases of flight. After a loss of several frequencies leading to a single frequency degraded mode, a receiver can use code and carrier phase pseudoranges made on only one carrier frequency to estimate the ionospheric delay. To achieve this estimation, the receiver can use the difference between code and carrier phase measurements. Indeed, this quantity can be modelled as twice the ionospheric delay plus noise, multipath, and the carrier phase ambiguity. The ionospheric delay can then be extracted from this, provided the ambiguity is properly removed. This can be achieved after convergence of a Kalman Filter for example, but then cycle slips need to be monitored. The probability of a cycle slip to occur is low but not negligible for civil aviation purposes. Several causes of cycle slips may be identified. For instance multipath, dynamics, signal blockage and ionospheric scintillation may be sources of this type of rupture in carrier phase measurements. Cycle slips may have random magnitudes. Those ones have to be detected and corrected with a performance compliant with civil aviation requirements for integrity, continuity, accuracy and availability. This problem of cycle slip detection is a priority before analyzing the accuracy of the single frequency iono corrected pseudorange. We propose to follow the methodology exposed below to assess the performance of potential algorithms of detection (and estimation) of cycle slips. First, the cycle slip detection and correction ability will be defined by the smallest cycle slip detectable with a required probability of missed detection. This smallest detectable cycle slip implies a bias on position error depending on geometry. Therefore, availability of protection against cycle slips compatible with APV 1 and APV 2 for instance, depends on geometry and must be computed at every second. The main goal of this paper is to know exactly the impact of the capability of cycle slip detection algorithms on the availability of reliable single frequency iono corrected pseudoranges.

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.