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').

Hyperspectral data appear to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy as handling the significant size of the data presents a challenge for the user community. Efficient compression techniques are required, and lossy compression, specifically, will have a role to play, provided its impact on remote sensing applications remains insignificant. To assess the data quality, suitable distortion measures relevant to end-user applications are required. Quality criteria are also of a major interest for the conception and development of new sensors to define their requirements and specifications. This paper proposes a method to evaluate quality criteria in the context of hyperspectral images. The purpose is to provide quality criteria relevant to the impact of degradations on several classification applications. Different quality criteria are considered. Some are traditionnally used in image and video coding and are adapted here to hyperspectral images. Others are specific to hyperspectral data.We also propose the adaptation of two advanced criteria in the presence of different simulated degradations on AVIRIS hyperspectral images. Finally, five criteria are selected to give an accurate representation of the nature and the level of the degradation affecting hyperspectral data.

The European satellite navigation system GALILEO will provide radio-navigation 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”). The work presented in this paper studies the possibility of computing SISMA using a statistically robust algorithm, so as to reject wrong measurements and decrease ground system False Alarm rate (fault-free satellites flagged “DON’T USE”).

This paper studies a new spectral analysis strategy for detecting, characterizing and classifying the different “spectral st ructures” of an unknown stationary process. A “spectra l structure” is defined as a sinusoidal wave, a narrow band signal or a noise peak. The spectral analysis strategy is based on the use of several successive and complementary spect ral analyses. Then, the proposed methodology provides a way to calculate a “spectral identity card” of each spectral struct ure, similarly to a real I.D. card. This I.D. card including all information related to this structure results from th e fusion of intermediate cards, which are obtained from different spectral analysis al gorithms. The I.D. card permits the classification of the detected spectral structur e into one of the following four classes: Pure Frequency, Narr ow Band, Alarm and Reject.

Subband decomposition has been shown to achieve very good performances for frequency estimation, particularly when parametric methods are used. This paper introduces a subband multichannel autoregressive spectral estimation method allowing to exploit the knowledge of intercorrelations between subseries in order to improve frequency estimation performances. This method is detailled then applied to a signals composed by a sum of 2 close sinusoids embedded in noise. Simulation results illustrate the interest of the proposed method.