Spacecraft health monitoring and failure prevention are major issues in space operations. In recent years, machine learning techniques have received an increasing interest in many elds and have been applied to housekeeping telemetry data via semi-supervised learning. The idea is to use past telemetry describing normal spacecraft behaviour in order to learn a reference model to which can be compared most recent data in order to detect potential anomalies. This paper introduces a new machine learning method for anomaly detection in telemetry time series based on a sparse representation and dictionary learning. The main advantage of the proposed method is the possibility to handle multivariate telemetry time series described by mixed continuous and discrete parameters, taking into account the potential correlations between these parameters. The proposed method is evaluated on a representative anomaly dataset obtained from real satellite telemetry with an available ground-truth and compared to state-of-the-art algorithms.

This paper develops a Rao-Blackwellized particle filter with variational inference for jointly estimating state and time-varying parameters in non-linear state-space models (SSM) with non-Gaussian measurement noise. Depending on the availability of the conjugate prior for the unknown parameters, the joint posterior distribution of the state and unknown parameters is approximated by using an auxiliary particle filter with a probabilistic changepoint model. The distribution of the SSM parameters conditionally on each particle is then updated by using variational Bayesian inference. Experiments are first conducted on a modified nonlinear benchmark model to compare the performance of the proposed approach with other state-of-the-art approaches. Finally, in the context of GNSS multipath mitigation, the proposed approach is evaluated based on data obtained from a measurement campaign conducted in a street urban canyon.

This article investigates on the derivation of good log likelihood ratio (LLR) approximations under uncorrelated fading channels with partial statistical channel state information (CSI) at the receiver. While previous works focused mainly on solutions exploiting full statistical CSI over the normalized Rayleigh fading channel, in this article, a Bayesian approach based on conjugate prior analysis is proposed to derive LLR values that only uses moments of order one and two associated with the random fading coefficients. The proposed approach is shown to be a more robust method compared to the best existing approximations, since it can be performed independently of the fading channel distribution and, in most cases, at a lower complexity. Results are validated for both binary and M-ary modulations over different uncorrelated fading channels.

Hidden Markov models (HMM) have been widely used for sequence modeling, such as speech and proteins, where the sequential signal is modeled as a doubly stochastic process compound of a hidden sequence inferred from the observed one. HMM captures the temporal context of sequences through the model parameters. This work studies the anomaly detection problem in time series via the learning of HMM parameters from observable sequences. For this, the maximum likelihood estimation of normal sequences is used to learn the model that best characterizes the normal behavior of the observed signals. Then, the log-probability of test sequences is computed using the learned-HMM, where higher values indicate a high probability of being a normal sequence. As a case of study, the approach is applied to multitemporal remote sensing by using extracted indicators from 13 Sentinel-2 images of rapeseed crops. The detection performance is evaluated in terms of precision and recall, where the HMM-learning approach obtains comparable detection rates against classical anomaly detection methods.

The recent decades have seen an increasing interest in Medium Earth Orbit and Low Earth Orbit satellite constellations. However, there is little information on the delay variation characteristics of these systems and the resulting impact on high layer protocols. To fill this gap, this paper simulates a constellation that exhibits the same delay characteristics as the already deployed Iridium but considers closer bandwidths to constellation projects. We identify five major sources of delay variation in polar satellite constellations with different occurrence rates: elevation, intra-orbital handover, inter-orbital handover, orbital seam handover and Inter-Satellite Link changes. We simulate file transfers of different sizes to assess the impact of each of these delay variations on the file transfer. We conclude that the orbital seam is the less frequent source of delay and induces a larger impact on a small file transfers: the orbital seam, which occurs at most three times during 24 hours, induces a 66% increase of the time needed to transmit a small file. Inter-orbital and intra-orbital handovers occur less often and reduce the throughput by approximately ~ 8% for both low and high throughput configurations. The other sources of delay variations have a negligible impact on small file transfers, and long file transfers are not impacted much by the delay variations.

This paper introduces a new fusion method for magnetic resonance (MR) and ultrasound (US) images, which aims at combining the advantages of each modality, i.e., good contrast and signal to noise ratio for the MR image and good spatial resolution for the US image. The proposed algorithm is based on two inverse problems, performing a super-resolution of the MR image and a denoising of the US image. A polynomial function is introduced to model the relationships between the gray levels of the two modalities. The resulting inverse problem is solved using a proximal alternating linearized minimization framework. The accuracy and the interest of the fusion algorithm are shown quantitatively and qualitatively via evaluations on synthetic and experimental phantom data.

This article introduces a new class of recursive linearly constrained minimum variance estimators (LCMVEs) that provides additional robustness to modeling errors. To achieve that robustness, a set of non-stationary linear constraints are added to the standard LCMVE that allow for a closed form solution that becomes appealing in sequential implementations of the estimator. Indeed, a key point of such recursive LCMVE is to be fully adaptive in the context of sequential estimation as it allows optional constraints addition that can be triggered by a preprocessing of each new observation or external information on the environment. This methodology has significance in the popular problem of linear regression among others. Particularly, this article considers the general class of partially coherent signal (PCS) sources, which encompasses the case of fully coherent signal (FCS) sources. The article derivates the recursive LCMVE for this type of problems and investigates, analytically and through simulations, its robustness against mismatches on linear discrete state-space models. Both errors on system matrices and noise statistics uncertainty are considered. An illustrative multi-channel array processing example is treated to support the discussion, where results in different model mismatched scenarios are provided with respect to the standard case with only FCS sources.

Satellite systems typically use physical and link layer reliability schemes to compensate the significant channel impairments, especially for the link between a satellite and a mobile end-user. These schemes have been introduced at the price of an increase in the end-to-end delay, high jitter or out-of-order packets. This is show to have a negative impact both on multimedia and best-effort traffic, decreasing the Quality of Experience (QoE) of users. In this paper, we propose to solve this issue by scheduling data transmission as a function of the channel condition. We first investigate existing scheduling mechanisms and analyze their performance for two kinds of traffic : VoIP and best-effort. In the case of VoIP traffic, the objective is to lower both latency and jitter, which are the most important metrics to achieve a consistent VoIP service. We select the best candidate among several schedulers and propose a novel algorithm speciffically designed to carry VoIP over LEO constellations. We then investigate the performance of the scheduling policies on Internet-browsing trac carried by TCP, where the goal is now the maximize the users' goodput, and select the best candidate in this case.

Light detection and ranging (Lidar) single-photon devices capture range and intensity information from a 3D scene. This modality enables long range 3D reconstruction with high range precision and low laser power. A multispectral single-photon Lidar system provides additional spectral diversity, allowing the discrimination of different materials. However, the main drawback of such systems can be the long acquisition time needed to collect enough photons in each spectral band. In this work, we tackle this problem in two ways: first, we propose a Bayesian 3D reconstruction algorithm that is able to find multiple surfaces per pixel, using few photons, i.e., shorter acquisitions. In contrast to previous algorithms, the novel method processes jointly all the spectral bands, obtaining better reconstructions using less photon detections. The proposed model promotes spatial correlation between neighbouring points within a given surface using spatial point processes. Secondly, we account for different spatial and spectral subsampling schemes, which reduce the total number of measurements, without significant degradation of the reconstruction performance. In this way, the total acquisition time, memory requirements and computational time can be significantly reduced. The experiments performed using both synthetic and real single-photon Lidar data demonstrate the advantages of tailored sampling schemes over random alternatives. Furthermore, the proposed algorithm yields better estimates than other existing methods for multi-surface reconstruction using multispectral Lidar data.