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Patent

PROCÉDÉ ET SYSTÈME DE TRANSMISSION DE PAQUETS DE DONNÉES À TRAVERS UN CANAL DE TRANSMISSION (RA) À ACCÈS ALÉATOIRE

Authors: Zamoum Selma, Gineste Mathieu, Lacan Jérôme, Boucheret Marie-Laure and Dupé Jean-Baptiste

n° 071277 FR RQDLV 14-05-18 YTA-LRE, May 2018.

Digital communications / Space communication systems

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PROCÉDÉ D'ALLOCATION DE FRÉQUENCES DANS UN SYSTÈME DE RADIOCOMMUNICATION SATELLITAIRE MULTIFAISCEAUX, ET SYSTÈME ASSOCIÉ

Authors: Baudoin Cédric, Couble Yoann, Deleu Thibault, Dupé Jean-Baptiste and Chaput Emmanuel

n° 071015 FR RQDLV 26-02-18 BRU-AMA, February 2018.

Networking / Space communication systems

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Conference Paper

New Solutions on the Design of a Galileo Acquisition-Aiding Signal to Improve the TTFF and the Sensitivity

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

In Proc. Institute of Navigation International Technical Meeting & Trade Show (ION ITM), Reston, USA, January 29-February 1, 2018.

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The design of a new GNSS signal is always a trade-off between improving performance and increasing complexity, or even between improving different performance criteria. Position accuracy, receiver sensitivity (acquisition, tracking or data demodulation thresholds) or the Time-To-First-Fix (TTFF) are examples of those GNSS receivers performance criteria. Within the framework of Galileo 2nd Generation (G2G), adding a new signal component dedicated to aid the acquisition process on E1 can help to improve performance of GNSS receivers with respect to these criteria as it was shown in [1]. In order to create this new component, various aspects such as the spreading modulation, the data navigation content, the channel coding or the Pseudo-Random Noise (PRN) codes must be studied. To this end, this paper firstly proposes the study of new spreading modulations, and secondly, we investigate on PRN codes that can be well suited to the proposed Acquisition-Aiding signal.

Digital communications / Localization and navigation and Space communication systems

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Journal Paper

Performances Analysis of GNSS NLOS Bias Correction in Urban Environment Using a 3D City Model and GNSS Simulator

Authors: Kbayer Nabil and Sahmoudi Mohamed

IEEE Transactions on Aerospace and Electronic Systems, PP(99):1-1, 2016.

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The well-known conventional Least Squares (LS) and Extended Kalman Filter (EKF) are one of the most widely used algorithms in science and particularly in localization with GNSS measurements. However, these estimators are not optimal when the GNSS measurements become contaminated by non-Gaussian errors including multipath (MP) and non-line-of-sight (NLOS) biases. On the other hand, this kind of ranging measurements errors occurs generally in urban areas where GNSS-based potitionning applications require more accuracy and reliability. In this paper, we use additional information of the environment consisting of bias prediction from a 3D model and a GNSS simulator to exploit constructively NLOS measurements. We use this 3D GNSS simulator to predict lower and upper bounds of these biases. Then, we integrate this information in the position estimation problem by considering these biases as additive error and exploiting the bounds to end-up with a constrained state estimation problem that we resolve with existing Constrained Least Squares (CLS) and Constrained EKF (CEFK) algorithms. Experimental results using real GPS signals in Down-Town Toulouse show that the proposed estimator is capable of improving the positioning acuracy compared to conventional algorithms. Theoretical conditions have been established to determine the acceptable bias prediction error allowing better positioning performance than conventional estimators. Tests are conducted then to validate these conditions and investigate the influence of the bias prediction error on the localization performance by proposing new acuracy metrics.

Signal and image processing / Localization and navigation

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Automatic Data-Driven Spectral Analysis Based on a Multi-Estimator Approach

Authors: Martin Nadine and Mailhes Corinne

Elsevier, Signal Processing, vol. 146, pp. 112–125, January, 2018.

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In signal processing, spectral analysis is widely used but, whereas computing the power spectral density (PSD) by Fourier approaches is relatively easy, its analysis and reading are much more demanding espe- cially for spectrally rich signals. This paper presents an original method which automatically picks out and estimates the relevant spectral structures of an unknown random stationary process, embedded in an unknown non-white Gaussian noise. First, a statistical hypothesis test is applied to each local max- imum value of the estimated PSD to detect the potential spectral peaks of interest. Second, an original feature space is proposed for classifying and characterizing the detected structures. Then, one key idea of the proposed strategy is to use not only one spectral estimator but to combine the results of different ones, taking benefits of their good properties. Therefore the detection and classification steps are ap- plied to different spectral estimations. A last fusion step outputs a complete attribute vector, including a confidence index, for each detected structure. Another key idea of this data-driven approach is that all parameters are automatically set up without a priori knowledge. This approach is fully adapted to the preventive maintenance of complex systems, as illustrated in the paper.

Signal and image processing / Other

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Conference Paper

Early and Robust Detection of Oscillatory Failure Cases (OFC) in the Flight Control System : A Data Driven Technique

Author: Urbano Simone

In Proc. 55th AIAA Aerospace Sciences Meeting, Grapevine, Texas, USA, January 9-13, 2017.

The Oscillatory Failure Case (OFC) is the name given to a class of failures in the actuator servo loop that cause undesired oscillation of the control surface. The term undesired refers to the fact that these oscillations, even if they are extremely rare, could be coupled with a structural mode and thus must be taken into account for load computation. The structural design is influenced by the OFC amplitude and detection time and so, if we are able to detect quickly smaller and smaller OFC amplitudes we can reduce the overall structural weight with all the related benefits. The current Airbus servo loop principle is shown in Figure 1. The faulty behaviour of an electronic component or a mechanical failure inside the actuator control loop could lead to an OFC. In this study we simulated the OFC effects through the injection of a periodic signal at two specific points of the control loop: the …

Signal and image processing / Other

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Talk

Les produits d’intermodulation passifs (PIM) dans les charges utiles de satellites de télécommunication

Author: Sombrin Jacques B.

Seminars of TeSA, Toulouse, December 7, 2017.

Optimisation de la gestion des ressources de la voie retour d’un satellite multi-faisceaux

Author: Couble Yoann

Seminars of TeSA, Toulouse, December 7, 2017.

PhD Thesis

Fusion of AIS and Radar Data for Maritime Surveillance

Author: Manzoni Vieira Fábio

Defended on November 30, 2017.

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Cooperative systems used for vessel identification and localization in the context of maritime surveillance, such as the Automatic Identification System (AIS), are often coupled to systems that allow the observation of uncooperative ships such as the Synthetic Aperture Radar (SAR). The combination of information coming from the SAR image and AIS signals can improve the detection of some ships in dense environments, but also allows possible piracy scenarios to be identified. The most common approach for data fusion is the “fusion after detection”, where each system processes the raw data independently. In the context of AIS and Radar, three levels of fusion can be identified: 1) fusion of the raw data, 2) fusion of raw data from a system with the processed data (list of detection) from the other system, 3) fusion of the detection lists formed by the two systems. We will focus on the first two cases, since the last case has been more widely covered in the literature. After introducing the AIS and Radar systems for maritime surveillance, we present structure of AIS data and radar signals, as well as the signal processing used to decode these AIS signals or to produce a radar image. The second chapter presents the potential benefits of the joint use of raw data from both radar and AIS for ship detection. After having described the signal models associated with the unknown ship position, we investigate the detection problem using a Generalized Likelihood Ratio Test (GLRT). The theoretical performances of this test are evaluated and allow us to estimate the performance gain in comparison to a single RSO processing. These theoretical results are validated by Monte Carlo simulations using Receiver Operational Characteristics (ROC). The detection results obtained using the GLRT are encouraging. However, the time implementation of these methods for practical applications is complicated. We therefore proceed to a sub-optimal detector using raw data from the radar and a list of detections from the AIS system, leading to a more simple detection strategy. The third chapter studies the fusion of raw radar data with a list of ship positions, formerly provided by the AIS system. Since the ships are moving and the AIS and Radar measurements are not are not acquired at the same time instants, the ship positions have to be extrapolated. Two extrapolation cases are considered in this work: 1) extrapolation errors are lower than the radar resolution and do not have to be integrated in the model, 2) extrapolation errors are not negligible and have to be taken into account in the model. Contrary to the second chapter, four hypotheses can now be considered. Indeed, in addition to the classical detection scenarios by both systems, we can identify the cases where only one of the systems detects a ship, which corresponds to the situations where a ship does not transmit its AIS position or where a ship intentionally false its AIS position. The problem can then be formalized with two successive binary hypothesis tests. This process allows the information coming from AIS and radar data to be fused naturally, aleading to improved radar detection performance. A performance comparison of this detector that uses a priori information with conventional radar detection shows that it is less sensitive to the proximity to other ships and to the ship density of the considered scenario. The fourth chapter presents the signal simulator considered in this thesis to test the detection algorithms in different surveillance scenarios, i.e., a piracy ship hijacking scenario, an illegal cargo transshipment and a navigation in a dense environment.

Signal and image processing / Space communication systems

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PhD Defense Slides

Fusion of AIS and Radar Data for Maritime Surveillance

Authors: Manzoni Vieira Fábio, Vincent François, Tourneret Jean-Yves, Bonacci David, Spigai Marc, Ansart Marie and Richard Jacques

Defended on November 30, 2017.

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Cooperative systems used for vessel identification and localization in the context of maritime surveillance, such as the Automatic Identification System (AIS), are often coupled to systems that allow the observation of uncooperative ships such as the Synthetic Aperture Radar (SAR). The combination of information coming from the SAR image and AIS signals can improve the detection of some ships in dense environments, but also allows possible piracy scenarios to be identified. The most common approach for data fusion is the “fusion after detection”, where each system processes the raw data independently. In the context of AIS and Radar, three levels of fusion can be identified: 1) fusion of the raw data, 2) fusion of raw data from a system with the processed data (list of detection) from the other system, 3) fusion of the detection lists formed by the two systems. We will focus on the first two cases, since the last case has been more widely covered in the literature. After introducing the AIS and Radar systems for maritime surveillance, we present structure of AIS data and radar signals, as well as the signal processing used to decode these AIS signals or to produce a radar image. The second chapter presents the potential benefits of the joint use of raw data from both radar and AIS for ship detection. After having described the signal models associated with the unknown ship position, we investigate the detection problem using a Generalized Likelihood Ratio Test (GLRT). The theoretical performances of this test are evaluated and allow us to estimate the performance gain in comparison to a single RSO processing. These theoretical results are validated by Monte Carlo simulations using Receiver Operational Characteristics (ROC). The detection results obtained using the GLRT are encouraging. However, the time implementation of these methods for practical applications is complicated. We therefore proceed to a sub-optimal detector using raw data from the radar and a list of detections from the AIS system, leading to a more simple detection strategy. The third chapter studies the fusion of raw radar data with a list of ship positions, formerly provided by the AIS system. Since the ships are moving and the AIS and Radar measurements are not are not acquired at the same time instants, the ship positions have to be extrapolated. Two extrapolation cases are considered in this work: 1) extrapolation errors are lower than the radar resolution and do not have to be integrated in the model, 2) extrapolation errors are not negligible and have to be taken into account in the model. Contrary to the second chapter, four hypotheses can now be considered. Indeed, in addition to the classical detection scenarios by both systems, we can identify the cases where only one of the systems detects a ship, which corresponds to the situations where a ship does not transmit its AIS position or where a ship intentionally false its AIS position. The problem can then be formalized with two successive binary hypothesis tests. This process allows the information coming from AIS and radar data to be fused naturally, aleading to improved radar detection performance. A performance comparison of this detector that uses a priori information with conventional radar detection shows that it is less sensitive to the proximity to other ships and to the ship density of the considered scenario. The fourth chapter presents the signal simulator considered in this thesis to test the detection algorithms in different surveillance scenarios, i.e., a piracy ship hijacking scenario, an illegal cargo transshipment and a navigation in a dense environment.

Signal and image processing / Space communication systems

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Activity Report

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Toulouse Space Show

TeSA was present at the Toulouse Space Show 2018. 3 great days!

TeSA in Freiburg, Germany

Julien Lesouple, TeSA PhD, presented a paper at SSP 2018.

TeSA in Boston

Lorenzo Ortega, TeSA PhD, invited by Pau Closas (left) at Northeastern University.