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).

The Binary Offset Carrier planned for future GNSS signal, including several GALILEO Signals as well as GPS M-code, presents a high degree of spectral separation from conventional signals. It also greatly improves positioning accuracy and enhances multipath rejection. However, with such a modulation, the acquisition process is made more complex. Specific techniques must be employed in order to avoid unacceptable errors. This paper assesses the performance of three method allowing to acquire and track BOC signal unambiguously : The Bump-jumping technique, The "BPSK-like" technique and the subcarrier Phase cancellation technique.

This paper addresses the problem of group size estimation for hybrid satellite/terrestrial multipoint communications. Estimators based on the maximum likelihood principle are investigated. These estimators assume that a Nack suppression mechanism is implemented at transport layer. The performance of these estimators is studied theoretically and via simulations. The integration of an appropriate group size estimator in a transport mechanism is finally considered.

Geostationary satellites are an efficient way to provide a large scale multipoint communication service. In the context of reliable multicast communications, a new hybrid satellite/terrestrial approach is proposed. It aims at reducing the overall communication cost using satellite broadcasting only when enough receivers are present, and terrestrial transmissions otherwise. This approach has been statistically evaluated for a particular cost function and seems interesting. Then since the hybrid approach relies on Forward Error Correction, several practical aspects of MDS codes and LDPC codes are investigated in order to select a code.

The localization performance of a navigation system can be improved by coupling different types of sensors. This paper focuses on INS-GPS integration. INS and GPS measurements allow to dene a non-linear state space model, which is appropriate to particle ltering. This model being conditionally linear Gaussian, a Rao-Blackwellization procedure can be applied to reduce the variance of the estimates.

Subband decomposition has been successfully used in several applications in image and signal processing and telecommunication fields. It consists in presenting a signal at the input of a filterbank and then decimating (sampling rate decreasing) the obtained filtered signals. Some authors recently showed that it can be a powerful tool for spectral estimation, either for the classical one (based on the Fourier transform) or spectral estimation based on parametric modellings. The aim of this PhD is double: first, we present the motivations and drawbacks of parametric spectral estimation in the subbands and then we present several original methods able to take advantage of the good properties of subband decomposition while preventing some of its drawbacks. Particularly, some methods able to cancel spectral overlapping induced by sampling rate decreasing will be developped, as well as other methods permitting to increase the perfromances of spectral estimation using the intercorrelation between the sub-series descended from decimation.