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Conference Paper

Processed 5G Signals Mathematical Models for Positioning considering a Non-Constant Propagation Channel

Authors: Tobie Anne-Marie, Garcia Pena Axel, Thevenon Paul and Aubault-Roudier Marion

In Proc. Vehicular Technology Conference (VTC-Fall), Honolulu, Hawaii, USA, September 22-25, 2019.

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The objective of this paper is to determine the ranging performance of the upcoming fifth generation (5G) signal. In order to do so, it is required to define 5G correlator outputs mathematical models. 5G systems will use OFDM (Orthogonal Frequency Division Multiplexing) signals; in the literature, mathematical models of OFDM signals are developed at the different receiver signal processing stages. These models assumed that the propagation channel is constant over an OFDM symbol; nevertheless, an in-depth study of QuaDRiGa, a 5G compliant propagation channel simulator, invalidates this hypothesis. Therefore, in this paper, mathematical models are developed that take into account the channel evolution. The focus is given on correlator outputs and results are applied to the computation of 5G based pseudo range accuracy.

Digital communications / Localization and navigation

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Improving the estimation of the sea level anomaly slppe

Authors: Mailhes Corinne, Besson Olivier, Guillot Amandine and Le Gac Sophie

in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Hawaï, USA, 19-24 July 2020.

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Satellite altimeters provide sea level measurements along satellite track. A mean profile based on the measurements averaged over a time period is then subtracted to estimate the sea level anomaly (SLA). In the spectral domain, SLA is characterized by a power spectral density of the form one over a power of the frequency where the power (the slope) is a parameter of great interest for ocean monitoring. However, this information lies in a narrow frequency band, located at very low frequencies, which calls for some specific spectral analysis methods. This paper studies a new parametric method based on an autoregressive model combined with a warping of the frequency scale (denoted as ARWARP). A statistical validation is proposed on simulated SLA signals, showing the performance of slope estimation using this ARWARP spectral estimator, compared to classical Fourier-based methods. Application to Sentinel-3 real data highlights the main advantage of the ARWARP model, making possible SLA slope estimation on a short signal segment, i.e., with a high spatial resolution.

Signal and image processing / Earth observation

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Anomaly Detection on Mixed Time-Series using a Convolutional Sparse Representation with Application to Spacecraft Health Monitoring

Authors: Pilastre Barbara, Silva Gustavo, Boussouf Loïc, d'Escrivain Stéphane, Rodriguez Paul and Tourneret Jean-Yves

In Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelone, Spain, May 4-8, 2020.

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This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. This model, referred to as C-ADDICT, builds upon the experiences of our previous ADDICT algorithm. It can handle discrete and continuous data jointly, is intrinsically shift-invariant, and crucially, it encodes each input signal (either continuous or discrete) from a joint activation and uniform combinations of filters, allowing the correlation across the input signals to be captured. The performance of C-ADDICT, is evaluated on a representative dataset composed of real spacecraft telemetries with an available ground-truth, providing promising results.

Signal and image processing / Other

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Journal Paper

Performance Limits of GNSS Code-Based Precise Positioning : GPS, Galileo & Meta-Signals

Authors: Das Priyanka, Ortega Espluga Lorenzo, Vilà-Valls Jordi, Vincent François, Chaumette Eric and Davain Loïc

MDPI Sensors, vol. 20, issue 8, p. 2196, April, 2020.

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This contribution analyzes the fundamental performance limits of traditional two-step Global Navigation Satellite System (GNSS) receiver architectures, which are directly linked to the achievable time-delay estimation performance. In turn, this is related to the GNSS baseband signal resolution, i.e., bandwidth, modulation, autocorrelation function, and the receiver sampling rate. To provide a comprehensive analysis of standard point positioning techniques, we consider the different GPS and Galileo signals available, as well as the signal combinations arising in the so-called GNSS meta-signal paradigm. The goal is to determine: (i) the ultimate achievable performance of GNSS code-based positioning systems; and (ii) whether we can obtain a GNSS code-only precise positioning solution and under which conditions. In this article, we provide clear answers to such fundamental questions, leveraging on the analysis of the Cramér–Rao bound (CRB) and the corresponding Maximum Likelihood Estimator (MLE). To determine such performance limits, we assume no external ionospheric, tropospheric, orbital, clock, or multipath-induced errors. The time-delay CRB and the corresponding MLE are obtained for the GPS L1 C/A, L1C, and L5 signals; the Galileo E1 OS, E6B, E5b-I, and E5 signals; and the Galileo E5b-E6 and E5a-E6 meta-signals. The results show that AltBOC-type signals (Galileo E5 and meta-signals) can be used for code-based precise positioning, being a promising real-time alternative to carrier phase-based techniques.

Signal and image processing / Localization and navigation and Space communication systems

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Anomaly Detection in Mixed Telemetry Data Using a Sparse Representation and Dictionary Learning

Authors: Pilastre Barbara, Boussouf Loïc, d'Escrivain Stéphane and Tourneret Jean-Yves

Signal Processing, vol. 168, March, 2020.

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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.

Signal and image processing / Other

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LLR Approximation for Fading Channels Using a Bayesian Approach

Authors: Ortega Espluga Lorenzo, Aubault-Roudier Marion, Poulliat Charly, Boucheret Marie-Laure, Al Bitar Hanaa and Closas Pau

IEEE Communications Letters, Early Access March, 2020.

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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.

Digital communications / Localization and navigation and Space communication systems

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Talk

Learning hidden Markov models for anomaly detection in time series

Authors: Leon Lopez Kareth, Arguello Fuentes Henry, Tourneret Jean-Yves and Mouret Florian

Seminar of TeSA, Toulouse, March 4, 2020.

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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.

Signal and image processing / Earth observation

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On the impact of intrinsic delay variation sources on Iridium LEO constellation

Authors: Boubaker Amal, Chaput Emmanuel, Beylot André-Luc, Kuhn Nicolas, Dupé Jean-Baptiste, Sallantin Renaud and Baudoin Cédric

Seminar of TeSA, Toulouse, March 4, 2020.

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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.

Networking / Space communication systems

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Journal Paper

Scheduling flows over LEO constellations on LMS channels

Authors: Tauran Bastien, Lochin Emmanuel, Lacan Jérôme, Arnal Fabrice, Gineste Mathieu and Kuhn Nicolas

International Journal of Satellite Communications and Networking ISSN 1542-0981, online, February, 2020.

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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.

Networking / Space communication systems

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Bayesian 3D Reconstruction of Subsampled Multispectral Single-photon Lidar Signals

Authors: Tachella Julian, Altmann Yoann, Marquez Miguel, Arguello Fuentes Henry, Tourneret Jean-Yves and McLaughlin Stephen

IEEE Transactions on Computational Imaging, vol. 6, pp.208-220, 2020.

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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.

Signal and image processing / Earth observation

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ICASSP 2020

Conference talk of Barbara Pilastre, TeSA PhD, at ICASSP 2020

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Activity Report

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Pau Closas, Northeastern Univ Boston, in TeSA

TeSA welcomes him with a research fellowship for July!