Vector acquisition collects all signals in a given environment, attempting to better identify those that are weakest but most important for navigation and positioning services. This article describes the operation of this methodology incorporating a new implementation algorithm to more efficiently use multiple GNSS signals in challenging environments.

Data gathering and transmission through a communicating network can be obtained thanks to wireless sensor networks and observation satellites. Using both these technologies will allow mankind to build a sustainable future by understanding the world around. In recent years, space actors have demonstrated a will to reuse the developped technologies by creating multiple programs platforms and defining context-agnostic protocols. The goal of this thesis is to study the characteristics of several observation technologies to exploit their similarities. We analyse the existing technologies and architectures in several contexts. Then, we propose a networking architecture handling constraints of most commonly used systems in such a context. The main constraints of observation scenarios are due to the links intermittence and lack of network connectivity. We focus on a solution using the delay tolerant networking concept. In such a context, a path between source and destination might not exist at all time. That is why most proposed protocols send multiple copies of a message to increase the delivery ratio. We wanted to decrease network resource use while keeping a similar performance to increase network efficiency. After having proposed a common architecture, we focused on particularities of each network segment to solve problems locally. Concerning the satellite part, we focused specifically on memory management techniques. We considered a low earth orbit satellite with a limited on-board buffer, gathering data from gateways. The goal is then to select the most urgent messages even though the least urgent ones are sent back to the ground. On the terrestrial sensor network part, we focused on the decrease of network resource use. We used the history of encounters between nodes and analysed the influence of the proportion of memory allocated to acknowledgements on network performance. We achieved better performance than existing solutions and at lower cost. The proposed solutions can be deployed and applied in several applications.

Data gathering and transmission through a communicating network can be obtained thanks to wireless sensor networks and observation satellites. Using both these technologies will allow mankind to build a sustainable future by understanding the world around. In recent years, space actors have demonstrated a will to reuse the developped technologies by creating multiple programs platforms and defining context-agnostic protocols. The goal of this thesis is to study the characteristics of several observation technologies to exploit their similarities. We analyse the existing technologies and architectures in several contexts. Then, we propose a networking architecture handling constraints of most commonly used systems in such a context. The main constraints of observation scenarios are due to the links intermittence and lack of network connectivity. We focus on a solution using the delay tolerant networking concept. In such a context, a path between source and destination might not exist at all time. That is why most proposed protocols send multiple copies of a message to increase the delivery ratio. We wanted to decrease network resource use while keeping a similar performance to increase network efficiency. After having proposed a common architecture, we focused on particularities of each network segment to solve problems locally. Concerning the satellite part, we focused specifically on memory management techniques. We considered a low earth orbit satellite with a limited on-board buffer, gathering data from gateways. The goal is then to select the most urgent messages even though the least urgent ones are sent back to the ground. On the terrestrial sensor network part, we focused on the decrease of network resource use. We used the history of encounters between nodes and analysed the influence of the proportion of memory allocated to acknowledgements on network performance. We achieved better performance than existing solutions and at lower cost. The proposed solutions can be deployed and applied in several applications.

L'invention concerne un procédé et un dispositif d'évaluation prédictive de puissance d'intermodulation dans un dispositif électronique dans lequel une fonction prédictive f permet d'évaluer de façon prédictive des valeurs de puissance d'une composante d'intermodulation produite par une distorsion d'intermodulation d'un signal d'entrée, caractérisé en ce que la fonction prédictive f comprend une partie impaire V obtenue par multiplication d'une fonction impaire F, et d'une fonction G, obtenue par composition d'une fonction g paire à valeurs réelles positives et d'une fonction Q sous forme de série réelle comprenant au moins un terme de degré q appartenant aux réels non-entiers.

The Low Extra Delay Background Transport (LEDBAT) protocol is a recently standardized protocol that aims to offer a scavenger service (i.e. the goal is to exploit the remaining and unused capacity of a link). LEDBAT is a delay-based protocol mainly defined by two parameters: a target queuing delay and a gain. The RFC 6817 provides guidelines to configure both parameters that strongly impact on the LEDBAT behavior in terms of fairness with other protocols. However, these guidelines are questioned by several studies as they might lead to the generation of a non-LBE (Less-than-Best-Effort) traffic. This paper explores the set of optimal parameters allowing LEDBAT protocol to effectively perform as an LBE traffic. We conclude that the optimal couple of target and decrease gain is (5ms; 10). However, we observe that the aggregated use of optimized LEDBAT sources still disturb the overall traffic performance and that the exponential backoff is not an answer to this issue. As a result, we believe that additional strategies to limit the number of LEDBAT flows are required for integrating this protocol at a large scale.

In this paper, we consider the problem of IP packet scheduling over a GSE/DVB-S2 satellite link. Scheduling flows with QoS requirements has been widely addressed in the mobile field, especially in LTE and WiMAX, with emphasis on fairness, efficiency and dynamic adaptation to transmission conditions. We focus on the well-known empirical scheduling rules known as PF, M-LWDF and EXP-PF for both QoS and MODCOD scheduling, and present how they were adapted to GSE/DVB-S2 encapsulation. Some of the challenging issues yielded by DVB-S2 are tackled, such as joint scheduling of both QoS and MODCODs, concatenation of numerous user packets into one BBFrame and fairness issues introduced by the scheduling algorithm, especially when dealing with various transmission scenarios. We show the potential of our scheduling algorithm using several simulations.

In this paper, we derive an analysis for a capacity approaching design of serially concatenated turbo schemes with continuous phase modulation (CPM) and low density parity check (LDPC) codes based on Laurent decomposition. The proposed design is based on extrinsic information evolution and Gaussian approximation. By inserting partial interleavers between LDPC and CPM and allowing degree-1 variable nodes under a certain constraint we show that designed rates can operate very close to the maximum achievable rate. Furthermore, we discuss the selection of low complexity receivers for the same optimized profiles.

This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional nonlinear term, affecting the end members and contaminated by an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. The performance of the proposed strategy is first evaluated on synthetic data. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

Satellite navigation (GNSS) is a constant in our days. The number of applications that depend on it is already remarkable and is constantly increasing. With new applications, new challenges have also risen: much of the new demand for signals comes from urban areas where GNSS signal processing is highly complex. In this thesis the issue of weak GNSS signal processing is addressed, in particular at the first phase of the receiver processing, known as signal acquisition. The first axe of research pursued deals with the analysis and compensation of the Doppler effect in acquisition. The Doppler shift that is experienced by a user is one of the main design drivers for the acquisition module and solutions are proposed to improve the sensitivity-complexity trade-off typical of the acquisition process. The second axe of research deals with the characterization of differential GNSS detectors. After a first step of coherent integration, transition to postcoherent (noncoherent or differential) integration is required for acquiring weak signals. The quantification of the sensitivity of differential detectors was not found in literature and is the objective of this part of the research. Finally, the third axe of research is devoted to multi-constellation Collective Detection (CD). CD is an innovative approach for the simultaneous processing of all signals in view. Several issues related to CD are addressed, including the improvement of the CD search process and the hybridization with standard acquisition. Finally, the application of this methodology in the context of a multi-constellation receiver is also addressed, by processing simultaneously real GPS and Galileo signals.

Satellite navigation (GNSS) is a constant in our days. The number of applications that depend on it is already remarkable and is constantly increasing. With new applications, new challenges have also risen: much of the new demand for signals comes from urban areas where GNSS signal processing is highly complex. In this thesis the issue of weak GNSS signal processing is addressed, in particular at the first phase of the receiver processing, known as signal acquisition. The first axe of research pursued deals with the analysis and compensation of the Doppler effect in acquisition. The Doppler shift that is experienced by a user is one of the main design drivers for the acquisition module and solutions are proposed to improve the sensitivity-complexity trade-off typical of the acquisition process. The second axe of research deals with the characterization of differential GNSS detectors. After a first step of coherent integration, transition to postcoherent (noncoherent or differential) integration is required for acquiring weak signals. The quantification of the sensitivity of differential detectors was not found in literature and is the objective of this part of the research. Finally, the third axe of research is devoted to multi-constellation Collective Detection (CD). CD is an innovative approach for the simultaneous processing of all signals in view. Several issues related to CD are addressed, including the improvement of the CD search process and the hybridization with standard acquisition. Finally, the application of this methodology in the context of a multi-constellation receiver is also addressed, by processing simultaneously real GPS and Galileo signals.