Compressive spectral imagers reduce the number of sampled pixels by coding and combining the spectral information. However, sampling compressed information with simultaneous high spatial and high spectral resolution demands expensive high-resolution sensors. This paper introduces a model allowing data from high spatial/low spectral and low spatial/high spectral resolution compressive sensors to be fused. Based on this model, the compressive fusion process is formulated as an inverse problem that minimizes an objective function defined as the sum of a quadratic data fidelity term and smoothness and sparsity regularization penalties. The parameters of the different sensors are optimized and the choice of an appropriate regularization is studied in order to improve the quality of the high resolution reconstructed images. Simulation results conducted on synthetic and real data, with different compressive sampling imagers, allow the quality of the proposed fusion method to be appreciated.

Le dispositif de détection (1) comprend un module de construction de test (6) configuré pour définir un modèle de la position de la gouverne selon deux hypothèses de test dont une première hypothèse de test pour laquelle il n'existe pas de vibration et une deuxième hypothèse de test pour laquelle il existe une vibration, un capteur (7) pour acquérir une mesure de position de la gouverne, un module de décision (10) pour décider si la première hypothèse doit être rejetée ou retenue à l'aide d'un test statistique, un module d'estimation (14) pour estimer une amplitude et une durée de la vibration, un module de calcul (15) pour calculer un indicateur de confort et de pilotabilité, un module de comparaison (16) pour comparer l'indicateur de confort et de pilotabilité à une échelle de valeurs de criticité pour obtenir un indicateur de criticité et un module d'envoi (17) pour envoyer l'indicateur de criticité à un dispositif utilisateur (18).

Single-photon light detection and ranging (Lidar) data can be used to capture depth and intensity profiles of a 3D scene. In a general setting, the scenes can have an unknown number of surfaces per pixel (semi-transparent surfaces or outdoor measurements), high background noise (strong ambient illumination), can be acquired by systems with a broad instrumental response (non-parallel laser beam with respect to the target surface) and with possibly high attenuating media (underwater conditions). The existing methods generally tackle only a subset of these problems and can fail in a more general scenario. In this paper, we propose a new 3D reconstruction algorithm that can handle all the aforementioned difficulties. The novel algorithm estimates the broadening of the impulse response, considers the attenuation induced by scattering media, while allowing for multiple surfaces per pixel. A series of experiments performed in real long-range and underwater Lidar datasets demonstrate the performance of the proposed method.

The identification of nonlinear Wiener models (NWMs) for deterministic inputs and Gaussian noise is studied. We show that the nonparametric kernel regression estimation of the nonlinearity of a NWM (based on the Nadaraya-Watson kernel estimator) can be formulated as a parametric estimation problem leading to a Gaussian conditional observation model. This property allows us to derive the maximum likelihood estimators of the unknown parameters of the NWM, as well as the associated Cramer-Rao (CR) bounds. We finally derive a CR-like bound on the global mean squared error (MSE) of the estimated nonlinearity of a NWM. Numerical results obtained for a pulse wave input are presented and compared to the ones based on the Nadaraya-Watson kernel estimator.

How many times have ever asked yourself: "Can I trust my satellite experiments' outcome?". Performing experiments on real satellite system can either be (1) costly, as the radio resource may be scarce or (2) not possible, as you can hardly change the waveforms transmitted by the satellite platform. Moreover, assessing user applications QoE can hardly be done using only simulated environments while the QoS modeling of a satellite system can often lead to non-conclusive or ambiguous results. The aim of this paper is to bring out representative solutions allowing the networking community to drive consistent experiments using open-source tools. To this end, we compare Mininet and OpenSAND satellite emulator to a real satellite access provided by CNES. We consider VoIP traffic to analyze the trade-off between reliability of the results, ease of use and reproducibility of the experiments.

So far, the problem of unmixing large or multitemporal hyperspectral datasets has been specifically addressed in the remote sensing literature only by a few dedicated strategies. Among them, some attempts have been made within a distributed estimation framework, in particular relying on the alternating direction method of multipliers (ADMM). In this paper, we propose to study the interest of a partially asynchronous distributed unmixing procedure based on a recently proposed asynchronous algorithm. Under standard assumptions, the proposed algorithm inherits its convergence properties from recent contributions in non-convex optimization, while allowing the problem of interest to be efficiently addressed. Comparisons with a distributed synchronous counterpart of the proposed unmixing procedure allow its interest to be assessed on synthetic and real data. Besides, thanks to its genericity and flexibility, the procedure investigated in this work can be implemented to address various matrix factorization problems.

This research study is the result of a 3 years CIFRE PhD thesis between the Airbus design office (Aircraft Control domain) and TéSA laboratory in Toulouse. The main goal is to propose, develop and validate a software solution for the detection and diagnosis of a specific type of elevator and rudder vibration, called limit cycle oscillation (LCO), based on existing signals available in flight control computers on board in-series aircraft. LCO is a generic mathematical term defining an initial condition-independent periodic mode occurring in nonconservative nonlinear systems. This study focuses on the LCO phenomenon induced by mechanical freeplays in the control surface of a civil aircraft. The LCO consequences are local structural load augmentation, flight handling qualities deterioration, actuator operational life reduction, cockpit and cabin comfort deterioration and maintenance cost augmentation. The state-of-the-art for freeplay induced LCO detection and diagnosis is based on the pilot sensitivity to vibration and to periodic freeplay check on the control surfaces. This study is thought to propose a data-driven solution to help LCO and freeplay diagnosis. The goal is to improve even more aircraft availability and reduce the maintenance costs by providing to the airlines a condition monitoring signal for LCO and freeplays. For this reason, two algorithmic solutions for vibration and freeplay diagnosis are investigated in this PhD thesis. A real time detector for LCO diagnosis is first proposed based on the theory of the generalized likelihood ratio test (GLRT). Some variants and simplifications are also proposed to be compliant with the industrial constraints. In a second part of this work, a mechanical freeplay detector is introduced based on the theory of Wiener model identification. Parametric (maximum likelihood estimator) and nonparametric (kernel regression) approaches are investigated, as well as some variants to well-known nonparametric methods. In particular, the problem of hysteresis cycle estimation (as the output nonlinearity of a Wiener model) is tackled. Moreover, the constrained and unconstrained problems are studied. A theoretical, numerical (simulator) and experimental (flight data and laboratory) analysis is carried out to investigate the performance of the proposed detectors and to identify limitations and industrial feasibility. The obtained numerical and experimental results confirm that the proposed GLR test (and its variants / simplifications) is a very appealing method for LCO diagnostic in terms of performance, robustness and computational cost. On the other hand, the proposed freeplay diagnostic algorithm is able to detect relatively large freeplay levels, but it does not provide consistent results for relatively small freeplay levels. Moreover, specific input types are needed to guarantee repetitive and consistent results. Further studies should be carried out in order to compare the GLRT results with a Bayesian approach and to investigate more deeply the possibilities and limitations of the proposed parametric method for Wiener model identification.

We propose a new fusion method for magnetic resonance imaging (MRI) and ultrasound (US) data combining two inverse problems: MRI reconstruction using super-resolution and US image despeckling, using a model relating the two modalities through an unknown polynomial function. We demonstrate the accuracy of the proposed fusion algorithm by quantitative and qualitative evaluation using synthetic data. The resulting fused image is shown to have an improved signal to noise ratio and spatial resolution compared to the native MRI and US images.

Multipath (MP) remains the main source of error when using global navigation satellite systems (GNSS) in a constrained environment, leading to biased measurements and thus to inaccurate estimated positions. This paper formulates the GNSS navigation problem as the resolution of an overdetermined system whose unknowns are the receiver position and speed, clock bias and clock drift, and the potential biases affecting GNSS measurements. We assume that only a part of the satellites are affected by MP, i.e., that the unknown bias vector has several zero components, which allows sparse estimation theory to be exploited. The natural way of enforcing this sparsity is to introduce an ℓ1 regularization associated with the bias vector. This leads to a least absolute shrinkage and selection operator problem that is solved using a reweighted- ℓ1 algorithm. The weighting matrix of this algorithm is designed carefully as functions of the satellite carrier-to-noise density ratio ( C/N0 ) and the satellite elevations. Experimental validation conducted with real GPS data show the effectiveness of the proposed method as long as the sparsity assumption is respected.