Spectral analysis of sea level anomalies (SLA) is widely used in the altimetry community to understand the geophysical content of the measured signal, to assess and compare the outputs of different missions. Spectral content of SLA is used to characterize ocean at different scales and to estimate the instrumental noise. Based on the SLA spectrum, one can estimate the spectral slope at medium to large scales (relied to the Surface Quasi-Geostrophic (SQG) ocean dynamics theory) and the measurement noise (observed as a noise plateau at smallest scales). A previous contribution [1] has pointed out the weaknesses of spectral analysis based on Fourier transform, mainly due to : (1) the convolutive bias which results in a biased estimation of the slope, the bias being related to the kind of observation weighting temporal window used and (2) the high variance of estimation leading to averaging several spectral estimations and raising the question of stationarity. To overcome these two drawbacks, a parametric spectral analysis method is proposed. This method is based on Auto-Regressive (AR) modeling [2,3] which is known to provide a spectral estimation with a lower variance than the as outperforming Fourier-based methods in terms of variance, in the case of short observation windows, without any need for choosing a weighting temporal window. Moreover, in order to better match the SLA frequency contents on a log scale to match the log scale interest of the SLA frequency contents , warping is introduced as a preprocessing prior to spectral analysis as it is done in speech coding [4]. Comparisons between the proposed parametric method (called ARWARP) and classical Fourier Fourier-based methods have been performed on both simulated SLA signals obtained from theoretical spectra and real signals from a high-resolution altimeter SARAL/AltiKa at 40 Hz rate (Orbit – Range – Mean Sea Surface). Results on simulated SLA signals highlight the performance of the ARWARPmethod, in terms of bias and variance on spectral estimation. ARWARP can be applied on short segments of SLA signals, providing a local information of the ocean characteristics, which can be of promising use by the wider Cal/Val and altimetry science community.

La navigation par satellites tient une place de plus en plus importante dans notre vie. On peut penser par exemple aux véhicules autonomes ou aux péages par localisation, mais plus quotidiennement on peut remarquer que, là où il y a un peu moins de 10 ans on avait besoin d’acheter un récepteur GPS pour naviguer par satellite, aujourd’hui un simple smartphone suffit, proposant de surcroît une plus grande diversité d’utilisation (navigation piétonne, localisation d’objets perdus, Pokémon GO…). Paradoxalement, ces dernières utilisations se font la plupart du temps dans le scénario de navigation le plus complexe : l’environnement urbain. En effet, si l’on arrive plus ou moins bien à estimer la plupart des différents types d’erreurs de propagation des signaux satellites que nous présenterons, les multitrajets restent la source d’erreur la plus compliquée à gérer car très dépendante du scénario : géométrie et surfaces des bâtiments, obstacles temporaires ou feuillage des arbres par exemple. Nous proposerons une solution innovante qui consiste à traiter les erreurs de multitrajets comme des vecteurs de biais dont certaines composantes sont nulles (c.-à-d., nous supposons que certains canaux de propagation ne souffrent pas de multitrajet) et nous appliquerons donc des méthodes d’estimation dite « parcimonieuses ».

L’accès à Internet par satellite permet de relier certains lieux isolés, où le déploiement d’un réseau terrestre est impossible. L’utilisation de constellations de satellites pose cependant des problèmes spécifiques à ce type de réseau, comme des délais longs et variables, ou des forts taux d’erreurs, notamment entre le dernier satellite sur le chemin du message et le récepteur mobile au sol (canal LMS, Land Mobile Satellite). Pour palier ce canal difficile, des mécanismes de fiabilisation ont été introduits au niveau des couches liaison et physique. Ces mécanismes allient l’utilisation de codes FEC et des retransmissions afin d’optimiser l’utilisation du canal LMS. En ce qui concerne la couche transport, le protocole le plus couramment utilisé est TCP, mais ce dernier fonctionne très mal avec les mécanismes de fiabilisation du canal LMS. Lors de cette présentation, nous verrons pourquoi les performances de TCP s’effondrent dans le cas de communications par satellite, puis nous verrons les solutions proposées dans cette thèse pour améliorer les performances de TCP.

This paper aims at quantifying the impact of a default TCP option, known as Delayed Acknowledgment (DelAck), in the context of LEO satellite constellations. Satellite transmissions can suffer from high channel impairments, especially on the link between a satellite and a ground gateway. To cope with these errors, physical and link layer reliability schemes have been introduced, at the price of an increase of the end-to-end delay seen by the transport layer (e.g. TCP). Although DelAck is used to decrease the feedback path load and for overall system performance, the use of this option conjointly with satellite link layer recovery schemes might increase the delay and might be counterproductive. To assess the impact of this option, we drive simulation measurements with two well-deployed TCP variants. The results show that the performance gain depends on the variant used and that this option should be carefully set or disabled as a function of the network characteristics. DelAck has a negative impact on TCP variants which are more aggressive such as TCP Hybla, and should be disabled for these versions. However, it shows benefits for TCP variants less aggressive such as NewReno.

Recent trends in Global Navigation Satellite System (GNSS) applications in urban environments have led to a proliferation of studies in this field that seek to mitigate the adverse effect of non-line-of-sight (NLOS) phenomena. However, these methods reduce the availability of positioning in deep urban conditions. For such harsh urban settings, this paper proposes a methodology of constructive use of NLOS signals, instead of their elimination. We propose to compensate for the NLOS errors using a 3D GNSS simulator to predict the measurements bias and integrate them as observations in the estimation method. We investigate a novel GNSS positioning technique based on measurement similarity scoring of an array of position candidates. We improve this technique using an estimation of the uncertainty on the bias prediction by 3D modeling. Experiment results using real GNSS data in a deep urban environment confirm the theoretical sub-optimal efficiency of the proposed approach, despite it intensive computational load.

This paper investigates strategies to carry out delay tolerant services over LEO satellite constellations for mobile receiver. In this context, LEO constellations are characterized by important delay variations where propagation impairments are mostly localized on the Land Mobile Satellite (LMS) channel (i.e. on the last hop). To cope with this issue, distinct reliability schemes can be introduced at the physical or link layers. Although their capacity to cope with transmission errors has been demonstrated, these recovery schemes may induce a high jitter that could severely damage TCP's internal timers and reliability schemes. As a matter of fact, transport and link layers’ reliability schemes exhibit a clear discrepancy. Following temporal traces representing the delay between a mobile terminal and the last hop satellite from a LEO constellation, we assess how HARQ mechanisms impact on the RTO based retransmission and the duplicate acknowledgments of TCP. Based on ns-2 simulations, we propose a layer-2 buffer that let both link and transport layers to conjointly perform. Our evaluations show an end-to-end data rate increase and more generally illustrate the benefit of re-ordering packets at the link layer when link-layer erasure coding recovery mechanisms are used conjointly with TCP.

This paper investigates a new method for cardiac motion estimation in 2D ultrasound images. The motion estimation problem is formulated as an energy minimization with spatial and sparse regularizations. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated in terms of motion estimation and strain accuracy and compared with state-of-the-art algorithms using a dataset of realistic simulations. These simulation results show that the proposed method provides very promising results for myocardial motion estimation.

Les composants électroniques destinés à l’échantillonnage haute fréquence des signaux ont des temps de réponse susceptibles de dégrader les performances des algorithmes de reconstruction. Dans cet article, on considère un signal échantillonné par un composant modélisé par un filtre linéaire invariant activé à des instants potentiellement irréguliers et qui forment une suite vérifiant la condition de Landau (généralisation de la condition de Nyquist). On étudie les conséquences des imperfections des échantillonneurs dans la reconstruction du signal et dans l’estimation de l’enveloppe et de la phase.

Dans cet article, nous proposons d’étudier différents algorithmes d’égalisation pour signaux à Modulation de Phase Continue (continuous phase modulation, CPM) dans le domaine fréquentiel à l’aide d’une représentation polyphase du signal. Par cette approche unifiée, nous montrons l’équivalence analytique de ces égaliseurs sous certaines hypothèses liées au canal de transmission, les liens, ainsi que leurs limitations dans certains cas.