Sea state dependent Doppler spread as a limit of coherent GNSS reflectometry from an airborne platform

Sea level rise and sea state variability due to climate change and global warming are major research topics in the scientific community. Wind speed (WS) and significant wave height (SWH) are usable parameters to monitor sea state threats and the impact of ocean weather conditions in coastal areas. GNSS reflectometry (GNSS-R) has shown considerable promise as a remote sensing technique for ocean parameter estimation. Multiple studies have been successfully conducted in the recent two decades by using GNSS-R ground-based, airborne and spaceborne data to retrieve geophysical properties of the sea surface. The focus of this study is to investigate the Doppler shift of the reflected signal as observable to estimate the Doppler spread (DS) and determine its correlation with sea state changes, making use of GNSS-R airborne data in coastal areas. An additional aim is to study the possibility of using the Doppler spread as a metric for coherent GNSS reflectometry for applications such as precise altimetry and precise total electron content (TEC) estimates. An experiment was conducted from the 12th to the 19th of July 2019 along Opal Coast, between the cities of Calais and Boulogne-sur-Mer, France. The experiment consisted of multiple flights at an altitude of ~780m (a.m.s.l). The direct and reflected signals were received by dual-polarized (Right-Handed and Left-Handed Circular Polarizations) antenna mounted on a gyrocopter. A software receiver is used to process the direct and reflected signals from the right-hand channel. The resulting in-phase (I) and quadrature (Q) components (at 50 Hz rate) of the reflected signals are analyzed in the spectral domain every ten seconds to obtain the relative Doppler shift and power estimates. The coherence is established by analyzing the phase observations obtained from I and Q. The sensitivity of the reflected signal estimates and the sea state is determined by the correlation between the Doppler Spread with wind speed and significant wave height. The latter two were obtained from the atmospheric, land and oceanic climate model, ERA5, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Initial results have shown promising performance at a calm sea (WS: 2.9 m/s and SWH: 0.26 m) and grazing angles. Satellites with low elevations (E 30°). The DS increases up to 2.1 Hz and the correlation decrease to 0.55 and 0.42 respectively. Coherence conditions are still under study; however, preliminary phase analysis reveals coherent observations at events with elevations below 15° and sea state with a significant wave height of 0.26 m

Atmospheric effects resolved in coherent airborne GNSS reflectometry

The advent of the Global Satellite Navigation Systems (GNSS) allowed the emergence of multiple satellites, airborne and terrestrial systems for remote sensing, and Earth observation. GNSS signals, designed for positioning and navigation, can be used to obtain characteristics of the Earth's surface once they get reflected. Recent studies have made use of GNSS Reflectometry as a tool for atmospheric sounding with promising results, broadening the spectrum for the use of the reflected GNSS signals. This study examines the occurrence of high-precision phase delay information for airborne reflectometry under the condition of various sea states in coastal waters. We fuse GNSS and ancillary data to resolve the tropospheric residual from the signal path change over the observed period. The experiment consisted of four flights performed with a gyrocopter in July 2019 along the coast between Calais and Boulogne-Sur-Mer, France. The processing comprises the integration of aircraft trajectory, GNSS satellites orbits, and geoid model for direct and reflected signal path difference modeling. Path predictions are used for GNSS-R data processing by means of a model-aided software receiver. The resulting reflected signal is passed through a retracking module to obtain the corrected Doppler shift and residual phase observable comparable with the tropospheric residual retrieved from ray-tracing modeling assuming a standard atmosphere. Initial results have shown promising performance at calm sea and grazing angles. Satellites with low elevations (E < 10°) reveal coherent observations that allow resolving tropospheric effects from GNSS-R airborne data

Atmospheric effects resolved in airborne GNSS reflectometry by data fusion processing

The advent of the Global Satellite Navigation Systems (GNSS) allowed the emergence of multiple satellite, airborne and terrestrial systems for remote sensing and Earth observation that make use of GNSS signals for navigation and positioning. However, GNSS signals can be also used as a remote sensing technique to obtain characteristics of the Earth's surface once they get reflected. This technique is nowadays called GNSS Reflectometry (GNSS-R) and offers different applications such as sea state, soil moisture, and sea ice concentration. GNSS reflectometry relies on bistatic radar configuration. Therefore, it is necessary to integrate multiple data sources to produce more accurate, useful, and consistent information from the transmitter-surface-receiver interaction. In this study, we fuse GNSS and ancillary data to resolve the tropospheric residual from the signal path change over the observed period. The experiment consisted of four flights performed with a gyrocopter in July 2019 along the coast between Calais and Boulogne-Sur-Mer, France. The processing comprises the integration of aircraft trajectory, broadcasted GNSS satellites orbits, and geoid model for direct and reflected signal path difference modeling. The latter is used for GNSS-R data processing by means of a model-aided software receiver. The resulting reflected signal is passed through a retracking module to obtain the corrected phase residual observable comparable with the tropospheric residual retrieved from ray-tracing modeling. Initial results have shown promising performance at calm sea and grazing angles. Satellites with low elevations (E < 10°) reveal coherent observations that allow resolving atmospheric effects from GNSS-R airborne data

Sea state-dependent Doppler spread as a limit of coherent GNSS reflectometry from an airborne platform

Sea level rise and sea state variability due to climate change and global warming are major research topics in the scientific community. Ocean weather conditions considerably impact coastal areas, and wind speed (WS) and significant wave height (SWH) are usable parameters to monitor the sea state threats on the coasts. GNSS reflectometry (GNSS-R) has shown considerable promise as a remote sensing technique for ocean parameters estimation. Multiple studies have been conducted successfully in the recent two decades by using GNSS-R ground-based, airborne and spaceborne data to retrieve geophysical properties of the ocean surface. The focus of this study is to investigate the Doppler shift of the reflected signal as observable to estimate the Doppler spread (DS) and determine its correlation with sea state changes employing GNSS-R airborne data in coastal areas. An additional aim is to study the possibility of using the Doppler spread as a metric for coherent GNSS reflectometry for applications such as precise altimetry and precise total electron content (TEC) estimates. An experiment was conducted from the 12th to the 19th of July 2019 along Opal Coast, between the cities of Calais and Boulogne-sur-Mer, France. The experiment consisted of multiple flights at an altitude of ~780m (a.m.s.l), and the direct and reflected signals were received by dual-polarized (Right-Handed and Left-Handed Circular Polarizations) antenna mounted on a gyrocopter. A software receiver is used to process the direct and reflected signals from the right-hand channel. The resulting in-phase (I) and quadrature (Q) components (at 50 Hz rate) of the reflected signals are analyzed in the spectral domain every ten seconds to obtain the relative Doppler shift and power estimates. The coherence is established by analyzing the phase observations obtained from I and Q. The sensitivity of the reflected signal parameters and the sea state is determined by the correlation between the Doppler Spread with wind speed and significant wave height. The latter two were obtained from the atmospheric, land and oceanic climate model, ERA5, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Initial results have shown promising performance at a calm sea (WS: 2.9 m/s and SWH: 0.26 m) and grazing angles. Satellites with low elevations (E 30°). The DS increases up to 2.1 Hz and the correlation decrease to 0.55 and 0.42 respectively. Coherence conditions are still under study; however, preliminary phase analysis reveals coherent observations at events with elevations below 15° and sea state with a significant wave height of 0.26 m

Airborne Coherent GNSS Reflectometry and Zenith Total Delay Estimation over Coastal Waters

High-precision GNSS (global navigation satellite e system) measurements can be used for remote sensing and nowadays play a significant role in atmospheric sounding (station data, radio occultation observations) and sea surface altimetry based on reflectometry. A limiting factor of high-precision reflectometry is the loss of coherent phase information due to sea-state-induced surface roughness. This work studies airborne reflectometry observations recorded over coastal waters to examine the sea-state influence on Doppler distribution and the coherent residual phase retrieval. From coherent observations, the possibility of zenith total delay inversion is also investigated, considering the hydrostatic mapping factor from the Vienna mapping function and an exponential vertical decay factor depending on height receiver changes. The experiment consists of multiple flights performed along the coast between the cities of Calais and Boulogne-sur-Mer, France, in July 2019. Reflected signals acquired in a right-handed circular polarization are processed through a model-aided software receiver and passed through a retracking module to obtain the Doppler and phase-corrected signal. Results from grazing angle observations (elevation < 15°) show a high sensitivity of Doppler spread with respect to sea state with correlations of 0.75 and 0.88 with significant wave height and wind speed, respectively. An empirical Doppler spread threshold of 0.5 Hz is established for coherent reflections supported by the residual phase observations obtained. Phase coherence occurs in 15% of the observations; however, the estimated zenith total delay for the best event corresponds to 2.44 m, which differs from the typical zenith total delay (2.3 m) of 5%

Normalized GNSS Interference Pattern Technique

It is well known that water level and snow height can be monitored with the ground reflectometry GNSS-R approach [1, 2]. In this approach the antenna situated on a mast, receives a direct GNSS signal coming from the satellite and a nadir signal reflected by the observed surface. Assuming that the antenna position is known we can compute the position of the surface of reflection. For water level monitoring and snow determination, this approach provides precise localization and dating of the measures that allows to process spatio-temporal comparison of water level and snow cover, respectively. These parameters are very important for flood monitoring, avalanche prevention, as well as for hydroelectic companies. Furthermore the approach is noninvasive and can be easily implemented on a portable instrument and embedded in a vehicle with a mast. The Interference Pattern Technique considers the behavior of the SNR of the received GNSS signal as a function of the satellite elevation [1]. The received signal is indeed the integration by the antenna of the direct and nadir reflected GNSS signals. Due to their different phase variations, the SNR oscillates at a rate proportional to the height between the antenna and the surface of specular reflection. Unfortunately the measurement is typically very long because it needs to process the SNR for high satellite elevation variations. We indeed need to observe a sufficient number of SNR oscillations to estimate the frequency and derive the surface height. In order to reduce the estimation time to a fraction of one period of the SNR variation, we propose to normalize the measures. The normalization consists in varying the antenna height of a value dh in order to read the minimum and maximum value of SNR for a given satellite elevation, and then in processing with these values the SNR measured for different satellite elevations. We show in this paper that the normalization allows to compute the cosine of the phase delay between the direct and reflected signals and to estimate the signal frequency on a fraction of a period. We also derive the minimum antenna variation range dh as a function of the satellite elevation. We deduce from this function the minimum time of observation as a function of the satellite elevation rate. We derive the exact evolution of the SNR as a function of the signals parameters (Doppler frequency, code delay, CN0) of the visible satellites [3]. The proposed method is assessed on real and synthetic signals

Universal-SBAS: A worldwide multimodal standard

This paper describes a generalisation of the aeronautical GNSS Space Based Augmentation System (SBAS) air interface, in a true worldwide multimodal standard named Universal S-BAS. Examples of usages of this multifrequency future standard are presented in the area of science and precise positioning, timing, security, robust positioning, maritime and reflectometry applications

Estimation et détection conjointe pour la fusion d'informations

The detection of change points or abrupt changes in the statistical parameters of a temporal series is a research problem in signal processing. The problem occurs in numerous applications domain like GNSS signal processing. The scientific project described in my research habilitation dissertation concerns the join estimation and detection of change points in the context of information fusion. The proposed approaches are used in different applications like the wind vector estimation, the GNSS signal processing and the processing of measurements provided by inertial sensors. The aim of information fusion is to use the measurements and information provided by different sensors to obtain a better knowledge of the parameters to estimate. We have proposed in this study several methods of join estimation and detection of changes that fused the information provided by the sensors. The goal of the proposed approach was to improve with fusion the performances in terms of change detection, change instant localization and change magnitude estimation. The proposed approach, defined in a Bayesian framework, relies on the definition of the a posteriori distribution of the parameters to estimate knowing the sensors measurements. One of our contributions in this context is to propose several filters defined in the circular domain with the normal circular distribution of von Mises. Another contribution is to propose a prior law that models the join behavior between several processes. This law is used to design an off line changes points and multi-processes estimate. This work was mainly applied to GNSS positioning and more specifically to the processing of the phase and code of the multi-carrier GNSS signals. We show with some experiments on real signals that the proposed methods allow to obtain a centimeter position accuracy for a receiver movement of strong dynamic. The prospects for this work are in the processing of the GNSS reflectometry signals and the underwater localization of objects. For these applications the broadcast signals are indeed strongly attenuate and distorted by the environment of propagation. In this context the estimation of their parameters constitute a new issue for the information fusion and signal processing technics.La détection des changements ou ruptures dans les paramètres de la distribution statistique d'une série temporelle est un problème de recherche en traitement du signal qui trouve des applications dans de nombreux domaines comme le traitement des signaux GNSS. Le projet scientifique développé dans mon mémoire d'habilitation à diriger des recherches concerne l'étude des méthodes d'estimation et de détection conjointe de ruptures pour la fusion d'informations. Les approches proposées dans ce cadre sont utilisées dans différentes applications comme l'estimation des paramètres du vecteur vent, le traitement des signaux GNSS et le filtrage des mesures fournies par les capteurs inertiels. L'objectif de la fusion est d'utiliser les informations ou mesures fournies par différents capteurs pour avoir une meilleure connaissance du paramètre à estimer. Nous avons donc proposé dans ce travail plusieurs méthodes d'estimation et de détection conjointe de rupture qui fusionnent les informations fournies par des capteurs. Le but étant d'améliorer les performances des opérateurs en termes de détection, de localisation et d'estimation de la dynamique des ruptures. Les approches proposées dans un cadre bayésien s'appuient sur la définition de la distribution a posteriori des paramètres à estimer sachant les mesures multi-capteurs. Un des apports de nos travaux dans ce domaine est de proposer un ensemble de filtres définis dans le domaine circulaire avec la distribution de von Mises. Un autre apport est de proposer une loi a priori qui modélise le comportement mutuel entre plusieurs processus dans le cas de la segmentation multi-ruptures hors ligne de signaux multi-capteurs. Ce travail a principalement été appliqué au géo positionnement et plus particulièrement au traitement de la phase et du code des signaux GNSS multi-porteuses. Nous avons montré par des expérimentations sur signaux réels que les méthodes proposées permettent d'obtenir une localisation centimétrique à la milliseconde d'un récepteur ayant un mouvement de forte dynamique. Aujourd'hui les perspectives de ce travail sont dans le traitement des signaux de réflectométrie GNSS et de géolocalisation sous-marine. En effet pour ces applications les signaux sont fortement atténués et déformés par le milieu de propagation, ce qui constitue un nouvel enjeu pour les méthodes de fusion d'informations en traitement du signal

Segmentation de données directionnelles

Nous proposons dans cette communication un estimateur MAP pour la segmentation et l'estimation simultanées d'un processus aléatoire stationnaire par morceaux, suivant une distribution de Von Mises. L'estimateur MAP se présente sous la forme d'une fonction de contraste pénalisée composée d'un terme d'attache aux données et d'un terme de régularisation. La segmentation est alors réalisée en minimisant la fonction de contraste par un algorithme de recuit simulé. Nous présentons l'implantation numérique de cet estimateur dans le cas de la distribution circulaire de Von Mises, l'évaluation des performances de la détection et son utilisation pour la segmentation de la direction du vent au sens de l'écart type angulaire et de la direction moyenne

Systèmes de fusion pour la segmentation hors-ligne de signaux GPS multi-porteuses

Le système de positionnement GPS (Global Positioning System) permet à tout utilisateur de déterminer sa position sur la Terre. Un récepteur calcule les temps de propagation que mettent les ondes électromagnétiques émises par une constellation de satellites pour lui parvenir. L estimation de ce temps de propagation est obtenue grâce à un signal discriminant, construit à partir de la corrélation des codes du signal reçu avec les codes générés par le récepteur. Actuellement, ces ondes ne sont émises que sur une unique fréquence porteuse pour une utilisation civile. Avec l évolution future du système GPS, ainsi que la mise en place du système européen Galiléo, plusieurs fréquences et donc plusieurs mesures de discriminants seront disponibles. Le travail présenté dans cette thèse a pour objet l étude et la mise en œuvre de méthodes de fusion appliquées à la segmentation hors-ligne des discriminants de code. Pour cela, plusieurs approches de segmentations bayésiennes sont étudiées. Une première technique de fusion hybride permet de déterminer la présence d une rupture de stationnarité dans une fenêtre d observation et sur plusieurs signaux. La seconde méthode de fusion, hors-ligne et multi-ruptures, permet de déterminer les ruptures de linéarités de signaux grâce à la minimisation d une fonction de contraste pénalisée. Ces méthodes sont ensuite appliquées à l estimation de la distance entre un satellite et un récepteur, et évaluées sur signaux réels pour le calcul de la position d un récepteur toutes les millisecondes.The GPS (Global Positioning System) allows any user to calculate his position on Earth. A receiver measures the transit time of electromagnetic waves emitted by a satellites constellation. The transit time is estimated with discriminator values calculate with correlation of codes contained in the signals and generated by the receiver. Actually, the GPS signal is emitted on a single carrier frequency for a civil use. With the future GPS evolution, as well as the future European system Galileo, several frequencies and so several discriminator measurements will be available. The purpose of this thesis is the study and the implementation of fusion methods for off-line segmentation of code discriminators. In this context, we examine several bayesan segmentation approaches. We propose a first hybrid fusion technique that permits to determine a stationary change on several signals. The second off-line fusion method permits to determine the linear multi-changes on a multi-carrier GPS signals. This method is based on the minimisationof a penalized contrast function. These methods are evaluated for the estimation of the distance between a satellite and a receiver, and for the position calculationof a receiver, on real data, every millisecond.CALAIS-BU Sciences (621932101) / SudocSudocFranceF