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.

Le découpage en sous-bandes est réputé pour ses très bonnes performances en matière d’estimation fréquentielle, en particulier lorsqu’on utilise des méthodes paramétriques. Cet article présente une méthode d’estimation spectrale basée sur le découpage en sous-bandes et la modélisation auto-regressive multi-dimensionnelle qui permet d’exploiter la connaissance des inter-corrélations entre les signaux de sousbande afin d’améliorer les performances de l’estimation fréquentielle. Le principe de la méthode est présenté puis appliquée à la résolution de 2 fréquences très proches dans le cas de signaux composés de deux fréquences pures très proches noyées dans du bruit. Des simulations effectuées sur des données synthétiques illustrent les performances de ce nouvel estimateur qui ouvre des perspectives intéressantes.

This paper presents the framework and the activities of a PhD research work in progress supported by Alcatel Alenia Space in collaboration with TeSA and SUPAERO. It deals with Connection Admission Control (CAC) for Telecommunications Systems with adaptive links according to propagation conditions. Indeed, in high frequency bands communications, deep fadings may occur because of atmospheric propagation losses. The mitigation techniques used to counteract fades impacts the system capacity, therefore the CAC mechanism. The CAC which only uses current capacity information may lead to intolerable dropping of admitted connection, and thus breaches the QoS guarantees made upon connection acceptance. New CAC mechanisms shall be studied to take into account the capacity variation and the mitigation techniques (IFMT) developed to compensate the attenuation in Ka and above frequency range.

Subband decomposition has been shown to be a useful tool for spectral estimation, in particular when parametric methods have to be considered. Indeed, the loss of observed samples due to decimation can be compensated by the use of a suitable model, if available. This paper studies a subband multichannel autoregressive spectral estimation (SMASE) method. The proposed method decomposes the observed signal through an appropriate filter bank and processes the decimated signals by means of a multichannel autoregressive (AR) model. This model takes advantage of known correlations between different subband signals. This a priori knowledge allows to improve spectral estimation performance. Simulation results illustrate the interest of the proposed methodology for signals with continuous spectra and for sinusoids.

This paper addresses the problem of spec-tral analysis on radar measurements using high res-olution methods. These methods have already been shown to yield better results than Fast Fourier Trans-form (FFT) based methods for accuracy on detected frequencies and more particularly for frequency res-olution. In most applications, these performances are closely related to the performances of range and veloc-ity estimation. In the paper, theoretical study shows the interest of subband decomposition for improving per-formances of frequency estimation in the case of the use of High Resolution methods, while it is shown to be inefficient when using FFT-based algorithms. Some elements of computational cost are given, in order to compare fullband and subband processing when using Fast Least Square Autoregressive (AR) algorithm. Fi-nally, experimental results are given, showing the inter-est of subband decomposition within the frame of radar signal processing either for accuracy and resolution on frequency estimation.

Hyperspectral data appears to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy. Handling the significant size of hyperspectral data presents a challenge for the user community. To enable efficient data compression without losing the potentiality of hyperspectral data, the notion of data quality is crucial for the development of applications. To assess the data quality, quality criteria relevent to end-user applications are required. This paper proposes a method to evaluate quality criteria. The purpose is to provide quality criteria corresponding well to the impact of degradation on end-user applications. Several quality criteria adapted to hyperspectral context are evaluated. Finally, five criteria are selected to give a good representation of the degradation nature and level affecting hyperspectral data.

This paper addresses the problem of joint measures of range and velocity of moving targets using millimetre wave FMCW radar (in the 77 Ghz range) within the field of automotive applications. The proposed solution is to determine range and velocity using spectral estimation of downconverted signals, theoretically composed of multiple sine functions embedded in noise. As a consequence, their accuracy is closely related to the accuracy of frequency estimation. In this paper, High Resolution spectral analysis methods (such as Auto-Regressive or Prony modeling) are shown to strongly impact the technological design constraints of the radars. More precisely, for a given sampling frequency of the downconverted signal, these methods show their ability either to significantly reduce the bandwidth of the linear frequency modulated radar sweeps although keeping constant the frequency resolution, or, for a given technological design, increase the same figure of merit. Moreover, adequate pre-processing of the signal is described, yielding correction of some 'nasty' non-linear effects (VCO, mixers, ...) as well as denoising received signals. Theoretical study of the performances is given and illustrated on simulated and real signals (provided by the RadarNet project of the 5th Framework Program).