Due to its heavy-tailed and fully parametric form, the multivariate generalized Gaussian distribution (MGGD) has been receiving much attention in signal and image processing applications. Considering the estimation issue of the MGGD parameters, the main contribution of this paper is to prove that the maximum likelihood estimator (MLE) of the scatter matrix exists and is unique up to a scalar factor, for a given shape parameter. Moreover, an estimation algorithm based on a Newton-Raphson recursion is proposed for computing the MLE of MGGD parameters. Various experiments conducted on synthetic and real data are presented to illustrate the theoretical derivations in terms of number of iterations and number of samples for different values of the shape parameter. The main conclusion of this work is that the parameters ofMGGDs can be estimated using the maximum likelihood principle with good performance.

We investigate the design of a broadcast system in order to maximize throughput. This task is usually challenging due to channel variability. Forty years ago, Cover introduced and compared two schemes: time sharing and superposition coding. Even if the second scheme was proved to be optimal for some channels, modern satellite communications systems such as DVB-SH and DVB-S2 rely mainly on a time sharing strategy to optimize the throughput. They consider hierarchical modulation, a practical implementation of superposition coding, but only for unequal error protection or backward compatibility purposes. In this article, we propose to combine time sharing and hierarchical modulation together and show how this scheme can improve the performance in terms of available rate. We introduce a hierarchical 16-APSK to boost the performance of the DVB-S2 standard. We also evaluate various strategies to group the receivers in pairs when using hierarchical modulation. Finally, we show in a realistic case, based on DVB-S2, that the combined scheme can provide throughput gains greater than 10% compared to the best time sharing strategy.

This thesis aims to introduce multifrequency phase tracking algorithms operating in low C/N0 environment. The objective is to develop new structures whose tracking limits are lower than that of current algorithms used in mass market receivers. Phase tracking suffers from a lack of robustness due to the cycle slip phenomenon. Works have thus been focused on elaborating new phase unwrapping systems. To do so, two different tracking approaches were studied. First, we have developed new monofrequency tracking loops based on a conventional DPLL. These structures aim at predicting the discriminator output by analyzing, thanks to a polynomial model, the last output samples of either the discriminator or the loop filter. Once the discriminator output is predicted, the estimated value is pre-compensated so that the phase dynamics to be tracked is reduced as well as the cycle slip rate. Then, the unwrapping structure analyzing the loop filter outputs has been extended to multifrequency signals. Using a data fusion step, the new multifrequency structure takes advantage of the frequency diversity of a GNSS signal (i.e., proportionality of Doppler frequencies) to improve the tracking performances. Secondly, studies have been focused on developing a new multifrequency tracking algorithm using variational Bayesian filtering technique. This tracking method, which also uses the GNSS frequency diversity, assumes a Markovian phase dynamics that enforces the smoothness of the phase estimation and unwraps it.

This thesis aims to introduce multifrequency phase tracking algorithms operating in low C/N0 environment. The objective is to develop new structures whose tracking limits are lower than that of current algorithms used in mass market receivers. Phase tracking suffers from a lack of robustness due to the cycle slip phenomenon. Works have thus been focused on elaborating new phase unwrapping systems. To do so, two different tracking approaches were studied. First, we have developed new monofrequency tracking loops based on a conventional DPLL. These structures aim at predicting the discriminator output by analyzing, thanks to a polynomial model, the last output samples of either the discriminator or the loop filter. Once the discriminator output is predicted, the estimated value is pre-compensated so that the phase dynamics to be tracked is reduced as well as the cycle slip rate. Then, the unwrapping structure analyzing the loop filter outputs has been extended to multifrequency signals. Using a data fusion step, the new multifrequency structure takes advantage of the frequency diversity of a GNSS signal (i.e., proportionality of Doppler frequencies) to improve the tracking performances. Secondly, studies have been focused on developing a new multifrequency tracking algorithm using variational Bayesian filtering technique. This tracking method, which also uses the GNSS frequency diversity, assumes a Markovian phase dynamics that enforces the smoothness of the phase estimation and unwraps it.

This work studies the performance of position estimation for distress beacons using time of arrival and frequency of arrival measurements. The analysis is conducted for emergency signals modeled as pulses with sigmoidal transitions. This model has shown interesting properties for Cospas-Sarsat search and rescue signals. The modified Cram´er-Rao bounds of the symbol width, time of arrival, frequency of arrival, and position of this model are presented. Simulations conducted with realistic signals indicate good agreement between these bounds and the mean square errors of the estimated parameters.

We develop a method for the estimation of the location of sources from measurements at multiple frequencies, including wideband measurements, recorded by a linear array of sensors. We employ interpolation matrices to address unequal sampling at different frequencies and make use of the Kronecker theorem to cast the nonlinear least squares problem associated with direction of arrival estimation into an optimization problem in the space of sequences generating Hankel matrices of fixed rank.We then obtain approximate solutions to this problem using the alternating direction method of multipliers. The resulting algorithm is simple and easy to implement. We provide numerical simulations that illustrate its excellent practical performance, significantly outperforming subspace-based methods both at low and high signal-to-noise ratio.

Following the constant increase of the multimedia traffic, it seems necessary to allow transport protocols to be aware of the video quality of the transmitted flows rather than the throughput. This paper proposes a novel transport mechanism adapted to video flows. Our proposal, called QAIMD for video quality AIMD (Additive Increase Multiplicative Decrease), enables fairness in video quality while transmitting multiple video flows. Targeting video quality fairness allows improving the overall video quality for all transmitted flows, especially when the transmitted videos provide various types of content with different spatial resolutions. In addition, QAIMD mitigates the occurrence of network congestion events, and dissolves the congestion whenever it occurs by decreasing the video quality and hence the bitrate. Using different video quality metrics, Q-AIMD is evaluated with different video contents and spatial resolutions. Simulation results show that Q-AIMD allows an improved overall video quality among the multiple transmitted video flows compared to a throughput-based congestion control by decreasing significantly the quality discrepancy between them.

The use of Global Navigation Satellites System, better known by the acronym GNSS, in an urban environment has grown signicantly, especially with the advent of GNSS chips in mobile phones. However, the urban environment introduces many diculties in GNSS signal reception that can lead to position ?s errors of several tens of meters. We chose to answer these problems by using a 3D city model allowing to simulate a realistic propagation of the GNSS signal in urban environment. The rst part of our work regards the Non Line Of Sight problem, where we propose a navigation solution based on a 3D city model to estimate geometrical properties of NLOS measured by the receiver. In a second part, the 3D city model is used to estimate the bias coming from the multipath on the pseudorange measurement. Finally, the last part of our study provides a solution coupling the GNSS signal vectorial tracking method to the information produced from the 3D city model in order to improve the tracking in the context of strong GNSS signal power attenuation.

Satellite navigation signals demodulation performance is historically tested and compared in the Additive White Gaussian Noise propagation channel model which well simulates open areas. Nowadays, the majority of new applications targets dynamic users in urban environments; therefore the implementation of a simulation tool able to provide realistically GNSS signal demodulation performance in obstructed propagation channels has become mandatory. This paper presents the simulator SiGMeP (Simulator for GNSS Message Performance) which is wanted to provide demodulation performance of any GNSS signals in urban environment, as faithfully of reality as possible. The demodulation performance of GPS L1C/A, GPS L2C, GPS L1C and Galileo E1 OS signals simulated with SiGMeP in the AWGN channel model configuration is firstly showed. Then, the demodulation performance of GPS L1C simulated with SiGMeP in urban environments is presented using the Prieto channel model with two signal carrier phase estimation configurations: perfect signal carrier phase estimation and PLL tracking.