Cet article présente une extension d’une approche plug-and-play pour le recalage de nuages de points 3D. Le problème de recalage de nuages de points 3D est formulé comme un problème inverse, et une approche plug-and-play est utilisée pour conjointement débruiter et recaler les nuages de points. Dans cet article, nous proposons d’optimiser la transformation de recalage en exploitant la structure de groupe de Lie de la transformation rigide SE(3). Des expériences menées sur des nuages de points LiDAR sont présentées mettant en évidence l’amélioration de la méthode par rapport à une méthode existante.

Routing in nanosatellites swarms presents distinct challenges, including variable node availability, constrained bandwidth, and dynamic topology. Strategies like delay-tolerant networking (DTN) can be advantageous, as they adapt to intermittent connectivity by storing and forwarding data when connections are established. Moreover, geographic routing protocols that exploit satellite positions can improve efficiency, while machine learning approaches may optimize routing decisions based on changing network conditions. What about hybrid approaches that may combine some of these methods? Basically, the crucial question is where to begin. The primary challenge for nanosatellites network designers is to determine which routing strategies to test prior to deployment. Given the vast number of existing routing protocols, testing all of them is not possible. This problem motivates the present study, which share the authors' experiences on selecting the most suitable routing algorithms for a given nanosatellites swarm. In particular, the study reports how the use of graph theory metrics helps in restricting the set of routing algorithms to be considered for network characterization and protocol selection.

High resolution spectrometers are composed of different optical elements and detectors that must be modeled as accurately as possible. Specifically, accurate estimates of Instrument Spectral Response Functions (ISRFs) are critical in order not to compromise the retrieval of trace gas concentrations from spectral measurements. Currently, parametric models are used to estimate these response functions. However, these models cannot always take into account the diversity of ISRF shapes that are encountered in practical applications. This paper studies a new ISRF estimation method based on a sparse representation of the ISRF in a dictionary. The proposed method is shown to be very competitive when compared to parametric models, yielding up to one order of magnitude smaller normalized ISRF estimation errors. The method is applied to different high-resolution spectrometers, demonstrating its reproducibility for multiple remote sensing missions.

Leveraging carrier phase observations within Global Navigation Satellite Systems receivers allows centimeter-level positioning accuracy. However, carrier phase observations are significantly affected by additive noise, which is assumed to follow a von Mises distribution, thereby degrading the performance of phase-based positioning estimators. To improve the modeling of carrier phase observations, we propose a novel approach that constrains the parameters of the von Mises distri-bution-specifically, the angular location modeling the phase and its dispersion parameter $\kappa$ modeling the noise-to evolve within the Lie group space $S O(2) \times \mathbb{R}^{+}$. To estimate these parameters, we employ a Lie group maximum likelihood estimator, solved through a Newton algorithm on Lie groups. This approach demonstrates advantages in terms of robustness and precision, especially when dealing with a small number of observations, compared to traditional Euclidean-based methods.

Plug-and-play algorithms have shown impressive results on imaging inverse problems, such as registration, super-resolution, denoising and inpainting. These methods rely on a neural network denoiser to learn an implicit prior of the image to be estimated. This paper investigates a new plug-and-play approach for 3D point cloud registration, which is crucial for a wide range of applications such as urban planning, archaeology and autonomous vehicles. The 3D point cloud registration problem is formulated as an inverse problem whose unknowns are the image to be estimated and the transformation between the two point clouds. A plug-and-play approach using an alternating optimization strategy is proposed for solving the registration problem. Experiments conducted on synthetic data and Li-DAR point clouds are presented showing the potential of the method.

This research paper proposes adaptations of three state-of-the-art anomaly detection algorithms, (One-Class Support Vector Machine, Isolation Forest and Local Outlier Factor), for detecting abnormal behavior in ship trajectories in an unsupervised way. These algorithms are adapted and tested using a wide range of similarity measures built specifically for time series, such as Dynamic Time Warping. The proposed methods are first applied on synthetic Automatic Identification System datasets with available ground truth. Then, they are generalized to handle pairs of Automatic Identification System and radar trajectories to detect unexpected activities, such as route deviations, delays and entering prohibited zones. The performances of the proposed methods are shown to be competitive when compared to the state-of-the-art for abnormal ship behavior detection.

Sparse regularisation has proven its worth and effectiveness in many fields, such as medical imaging. In this sense, nuclear magnetic resonance spectroscopy (MRS) is one of the modalities that could greatly benefit from sparse regularisation. This paper introduces a novel Bayesian approach for MRS restoration that accounts for possible errors in the observation linear operator. The algorithm is tailored to the complex nature of MRS data, incorporating both real and imaginary parts of the spectrum. An MCMC (Markov chain Monte Carlo) inference is conducted using a Gibbs sampler strategy. The method has been successfully validated on both synthetic and clinical data of high-grade brain tumor glioblastoma (GBM) patients. This study will enable further analysis of metabolites of interest not conventionally considered in clinics because of their undetectable concentration.

This letter explores time-delay and Doppler estimation in the presence of unknown heavy-tailed disturbance. Conventional methods for achieving optimal mean squared error performance rely on the maximum likelihood estimator (MLE), which is consistent and asymptotically efficient under the unrealistic assumption of a perfect a-priori knowledge of the noise distribution. However, in practical situations, the noise distribution is often unknown, and classical parametric estimation procedures are no longer able to guarantee the statistical efficiency. In this work, by relying on the semiparametric theory, we present an original rank-based and distribution-free $R$-estimator which have the remarkable property to be parametrically efficient, i.e. it attains the “classical” Cramér-Rao Bound, irrespective of the unknown noise distribution, provided that the latter belongs to the family of Complex Elliptically Simmetric (CES) distributions.