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Article de journal

Clean-to-Composite Bound Ratio: A Multipath Criterion for GNSS Signal Design and Analysis

Auteurs : Lubeigt Corentin, Ortega Espluga Lorenzo, Vilà-Valls Jordi, Lestarquit Laurent et Chaumette Eric

IEEE Transactions on Aerospace and Electronic Systems, vol. 58, issue 6, pp. 5412-5424, December, 2022.

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Multipath is one of the most challenging propagation conditions affecting Global Navigation Satellite Systems (GNSS), which must be mitigated in order to obtain reliable navigation information. In any case, the random multipath nature makes it difficult to anticipate and overcome. Therefore, for legacy GNSS signal performance assessment, modern GNSS signal design and future GNSS-based applications, robustness to multipath is a fundamental criterion. Different multipath metrics exist in the literature, such as the multipath error envelope, usually leading to analyses only valid for a dedicated receiver/signal combination and only providing information on the bias. This paper presents a general criterion to characterize the multipath robustness of a generic band-limited signal (e.g., GNSS or radar), considering the joint delay-Doppler and phase estimation. This criterion is based on the Cramr-Rao bound, which makes it universal, regardless the receiver architecture and the signal under analysis, and provides information on the actual achievable performance in terms of estimated time-delay (i.e., pseudo-range) and Doppler frequency variances.

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Traitement du signal et des images et Réseaux / Localisation et navigation

A novel image representation of GNSS correlation for deep learning multipath detection

Auteurs : Blais Antoine, Couellan Nicolas et Evgenii Munin

Array, online, April, 2022.

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This paper proposes a novel framework for multipath prediction in Global Navigation Satellite System (GNSS) signals. The method extends from dataset generation to deep learning inference through Convolutional Neural Network (CNN). The process starts at the output of the correlation stage of the GNSS receiver. Correlations of the received signal with a local replica over a (Doppler shift, propagation delay)-grid are mapped into grey scale 2D images. They depict the received information possibly contaminated by multipath propagation. The images feed a CNN for automatic feature construction and multipath pattern detection. The issue of unavailability of a large amount of supervised data required for CNN training has been overcome by the development of a synthetic data generator. It implements a well-established and documented theoretical model. A comparison of synthetic data with real samples is proposed. The complete framework is tested for various signal characteristics and algorithm parameters. The prediction accuracy does not fall below 93% for C/N0 ratio as low as 36 dBHz, corresponding to poor receiving conditions. In addition, the model turns out to be robust to the reduction of image resolution. Its performance is also measured and compared with an alternative Support Vector Machines (SVM) technique. The results show the undeniable superiority of the proposed CNN algorithm over the SVM benchmark.

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Traitement du signal et des images / Localisation et navigation

Article de conférence

Close-to-Ground Single Antenna GNSS-R

Auteurs : Lubeigt Corentin, Vincent François, Ortega Espluga Lorenzo, Vilà-Valls Jordi, Lestarquit Laurent et Chaumette Eric

In Proc. ESA Workshop on Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing (NAVITEC), Online Event, 5-7 april 2022.

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For more than three decades, Global Navigation Satellite System (GNSS) signals have been seen as signals of opportunity as in GNSS Reflectometry (GNSS-R). The study of the reflections from the ground of such signals can indeed lead to many features regarding the reflecting surface and the receiver’s height. Due to the nature of the GNSS signal, that is, due to its wavelength, the distortion of the reflected signal may vary significantly depending on the reflecting surface and on the dynamic and height of the receiver. The latter does range from low earth orbit down to ground-based platforms. In this last case, the vicinity to the ground induces important interference between the direct and the reflected path which makes it difficult to process directly in order to obtain altimetry product. In this study, the feasibility of ground-based single antenna GNSS-R altimetry is studied and solutions are presented depending on the satellite elevation angle. To do so, maximum-likelihood-based algorithms - namely the CLEAN-RELAX Estimator and the Approximate Maximum Likelihood Estimator - are presented and applied to a set of scenarios.

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Traitement du signal et des images / Localisation et navigation

Mitigation of TACAN/DME interferences for L5/E5 space-borne GNSS receivers in LEO. First Simulation results with focus on Radio-Occultation missions

Auteurs : Lestarquit Laurent, Dulery Christelle, Prévost Raoul et Iervolino Mariano

NAVITEC, ESA/ESTEC, Noordwijk, The Netherlands, April 5-7, 2022.

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At their design time, GPS L5 and GALILEO E5a/E5b signals compatibility with TACAN/DME was studied for aeronautical users with altitude limited up to 40,000 feet, but not for space-borne users. Aircraft would see a few strong pulses and a mitigation technique as simple as pulse blanking would usually work as mitigation means. For space-borne GNSS receivers in Low Earth Orbit (LEO), if the larger free space losses lead to weaker received TACAN/DME signals, the number of beacons in visibility is much higher, reaching in the worst locations a total over two hundred with more than half of them having a peak power above or close to the noise floor, making time blanking a poor mitigation means. Therefore, other mitigation techniques performances need to be assessed in order to determine which techniques are best suited. A simulation tool was developed to compute the post correlation C/No degradation due to TACAN/DME on a LEO with and without mitigation means enabled. The equivalent post-correlation noise (No) increase due to TACAN/DME, or what remains after a mitigation technique is applied, is simulated using the Spectral Separation Coefficient (SSC) methodology to emulate the effect of GNSS signal de-spreading in the receiver correlation process. The part of the useful signal carrier suppressed by the application of a mitigation technique (time blanking and/or frequency notch filtering) is taken into account in the simulation. This study is focusing on radio-occultation (RO) missions which are the more sensitive to TACAN/DME interferences. Indeed, a medium-gain antenna (9–18 dB typical) is steered toward the earth limb resulting in having many TACAN/DME transmitters inside its main lobe. This configuration can lead to a high received power from them, as the LEO RO satellite is also in their main antenna lobe. In this configuration, the C/No degradation, plotted on a geographic map can reach up to 13.8 dB in the absence of mitigation over the European TACAN/DME hotspot. Several promising mitigation techniques have been included in the simulation tool to determine which one shall be implemented on board a LEO RO satellite mission: time domain pulse blanking, Frequency Domain Adaptive Filtering (FDAF) or hybrid blanking. We also considered implementing pulse cancellation, an attractive technique in theory, but not so in practice due to the deviation of the actual transmitted signals with respect to their theoretical models. As anticipated, pulse blanking does not perform well at the LEO orbit. It can be actually worse than doing nothing when there is a large number of TACAN/DME transmitters in visibility since it leads to a high loss in useful GNSS signals during the blanking process. As detailed in this paper, hybrid time domain and frequency domain methods are more effective when frequency notch filtering is applied over a limited time window . For FDAF, windows have fixed boundaries, independently of the presence of interfering pulses, whereas in the hybrid method, the time windows are centered on the detected pulses. The FDAF method reduced the peak interference down to 5.6 dB. The hybrid blanking method has the best performances with a worst degradation which can be reduced to 4.3 dB over the European hotspot.

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Traitement du signal et des images / Observation de la Terre et Localisation et navigation

Séminaire

Equivariant Imaging: learning to solve inverse problems without ground truth

Auteur : Tachella Julian

Seminar of TeSA, Toulouse, March 15, 2022.

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In recent years, deep neural networks have obtained state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography. Networks are generally trained with pairs of signals and associated measurements. However, in various imaging problems, we usually only have access to compressed measurements of the underlying signals, hindering this learning-based approach. Learning from measurement data only is impossible in general, as the compressed observations do not contain information outside the range of the forward sensing operator. In this talk, I will present a new learning framework, called Equivariant Imaging, which overcomes this limitation by exploiting the invariance to transformations (translations, rotations, etc.) present in natural signals. I will also discuss necessary and sufficient conditions for learning without ground truth. Our proposed learning strategy performs as well as fully supervised methods and can handle noisy data. I will show results on various inverse problems, including sparse-view X-ray computed tomography, accelerated magnetic resonance imaging and image inpainting.

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Traitement du signal et des images / Autre

Article de conférence

Multipath Estimating Techniques Performance Analysis

Auteurs : Lubeigt Corentin, Ortega Espluga Lorenzo, Vilà-Valls Jordi, Lestarquit Laurent et Chaumette Eric

In Proc. IEEE Aerospace Conference, Big Sky, MT, USA, March 5-12, 2022.

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In Global Navigation Satellite Systems, resilience to multipath remains an important open issue, being the limiting factor in several applications due to the environment specific nature of such harsh propagation conditions. In order to assess the multipath impact into the final system performance, accurate metrics are required. The multipath error envelope (MPEE), even if easy to handle, is limited to the study of the bias of a receiver architecture in a noise free environment. Moreover, when it is a flat zero-valued line, the MPEE becomes less informative about the parameter estimation performance. Considering an unbiased estimator, an alternative way to characterize an architecture is to evaluate its mean square error (MSE) and compare it to the corresponding Cram´er-Rao bound (CRB). In this work, a methodology to use both aforementioned tools is presented. First, the MPEE, which is an understandable metric. Secondly, the MSE convergence to the CRB, where one can clearly interpret the estimation performance in terms of signal-to-noise ratio or minimum path separation. These tools are then applied to a range of known multipath mitigation techniques. In addition, a new alternating projection multipath mitigation approach is proposed and analyzed.

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Traitement du signal et des images / Localisation et navigation

Article de journal

Generalized Frequency Estimator with Rational Combination of Three Spectrum Lines

Auteurs : Gigleux Benjamin, Vincent François et Chaumette Eric

IET Radar Sonar Navigation, pp. 1-9, March 2022.

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The popular Discrete Fourier Transform (DFT) is known to be a sub‐optimal frequency estimation technique for a finite transform length. In order to approach the Cramer‐Rao Lower Bound (CRLB), many refinement techniques have been considered, but little considering both zero padding or tapering, also known as windowing or apodisation. In this paper, a frequency estimator with closed‐form combination of three DFT samples is generalized to zero padding and tapered data within the class of cosine windowing. Root Mean Squared Error (RMSE) is shown to approach the CRLB in the case of a single tone signal with additive white Gaussian noise. Compared to state‐of‐the‐art techniques, the proposed algorithm improves the frequency RMSE up to 1 dB when using significant zero‐padding lengths (K ≥ 2 N) and for small to moderate SNR, which is the most challenging case for practical radar applications.

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Traitement du signal et des images et Réseaux / Systèmes de communication aéronautiques, Localisation et navigation et Systèmes spatiaux de communication

Séminaire

Signal Processing for GNSS-R

Auteur : Lubeigt Corentin

Seminar of TeSA, Toulouse, February 8, 2022.

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For more than three decades, Global Navigation Satellite System (GNSS) signals have been seen as signals of opportunity as in GNSS Reflectometry (GNSS-R). The study of the reflections from the ground of such signals can indeed lead to many features regarding the reflecting surface and the receiver's height. Due to the nature of the GNSS signal, that is, due to its wavelength, the distortion of the reflected signal may vary significantly depending on the reflecting surface and on the dynamic and height of the receiver. The latter does range from low earth orbit down to ground-based platforms. In this last case, the vicinity to the ground induces important interference between the direct and the reflected path which makes it difficult to process directly in order to obtain altimetry product. In this presentation, after a brief description of the main features of the GNSS-R problem, the feasibility of ground-based single antenna GNSS-R altimetry is studied.

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Traitement du signal et des images / Localisation et navigation

Improve Congestion Control mechanism with the help of Machine Learning

Auteur : Perrier Victor

Seminar of TeSA, Toulouse, February 8, 2022.

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TCP (Transmission Control Protocol) Congestion control mechanism is an essential part of internet communications: it manages how fast the information is sent between two end points. That mechanism aims to achieve a compromise between 3 goals. The first is to achieve the maximum throughput for each flows, the second goal is to reduce the latency between the server and the client, and the last goal is to achieve fairness between each flows. The compromise between these 3 goals is very hard to achieve with human heuristics and basic models because of the ever increasing complexity of internet topologies. We choose to investigate machine learning solution in order optimize the Congestion Control mechanism. In this presentation, the bases of congestion control and the impact of machine learning on that mechanism will be explained.

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Réseaux / Systèmes spatiaux de communication

Article de journal

Satellite Image Compression and Denoising With Neural Networks

Auteurs : Alves de Oliveira Vinicius, Chabert Marie, Oberlin Thomas, Poulliat Charly, Bruno Mickael, Latry Christophe, Carlavan Mikael, Henrot Simon, Falzon Frédéric et Camarero Roberto

IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, January, 2022.

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Earth observation through satellite images is crucial to help economic activities as well as to monitor the impact of human activities on ecosystems. Current satellite systems are subjected to strong computational complexity constraints. Thus, image compression is performed onboard with specifically tailored algorithms while image denoising is performed on the ground. In this letter, we intend to address satellite image compression and denoising with neural networks. The first proposed approach uses a single neural architecture for joint onboard compression and denoising. The second proposed approach sequentially uses a first neural architecture for onboard compression and a second one for on ground denoising. For both approaches, the onboard architectures are lightened as much as possible, following the procedure proposed by Alves de Oliveira et al. (2021). The two approaches are shown to outperform the current satellite imaging system and their respective pros and cons are discussed.

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Traitement du signal et des images / Observation de la Terre

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