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.

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.

Subband decomposition has already been shown to increase the performance of spectral estimators, but induced frequency overlapping may be troublesome, bringing edge effects at subband borders. A recent paper (Bonacci, D. et al., EUSIPCO, 2002) proposed a method (SDFW - subband decomposition and frequency warping) allowing subband decomposition to be performed without aliasing. We modify this subband decomposition in order to improve frequency resolution for any correlation based spectral estimator when applied to the subband outputs. Three main improvements are proposed: the subband decomposition is based on comb filters; the SDFW method warping operation is performed using a complex frequency modulation; the autocorrelation is estimated using all sub-series from each subband. Simulation results demonstrate the anticipated performance of the proposed method.

Satellite payloads are becoming much more complex with the evolution towards multimedia applications. Moreover satellite lifetime increases while standard and services evolve faster, necessitating a hardware platform that can evolves for not developing new systems on each change. The same problem occurs in terrestrial systems like mobile networks and a foreseen solution is the software defined radio technology. In this paper we describe a way of introducing this concept at satellite level to offer to operators the required flexibility in the system. The digital functions enabling this technology, the hardware components implementing the functions and the reconfiguration processes are detailed. We show that elements of the software radio for satellites exist and that this concept is feasible.

Subband decomposition has already been shown to increase the performances of spectral estimation but induced frequency overlapping may be troublesome, bringing edge effects, when spectral estimation is applied after subband decomposition and decimation. This paper proposes a new spectral estimation procedure based on subband decomposition and frequency warping which reduces the overlapping frequency problem. Simulation results confirm the interest of this new algorithm.

This paper addresses the problem of frequency overlapping which occurs when spectral estimation is applied after subband decomposition. Subband decomposition has already been shown to increase the performances of spectral estimation but induced frequency overlapping may be troublesome. This paper proposes a new spectral estimation procedure based on sub band decomposition and frequency warping which reduces the overlapping frequency problem. Simulations confirm the interest of this new algorithm.

This paper presents new concepts to address the air traffic complexity modeling problem. Two new geometrical metrics have been introduced and have been found very useful to capture typical features of traffic complexity. The covariance metric is very adapted to identify disorder in a set of speed vectors and can be applied for en route airspace (en route airspace is the airspace between airports). Similarly, the Koenig metric identifies easily the curl movement organizations and can be applied to areas around airports where air traffic control procedures impose turns on aircraft trajectories.