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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 the sea state threats and the impact of the ocean weather conditions in coastal areas. GNSS reflectometry (GNSS-R) has shown considerable promise as a remote sensing technique for ocean parameters 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 < 10°) present a Doppler Spread of 0.3 Hz and its Pearson correlations with respect to WS and SHW are 0.89 and 0.75, respectively. The performance is relatively poor for high elevation events (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.

Climate change has been a major worldwide concern over the last decades. One of its consequences is an acceleration of the coastal erosion in littoral areas such as the Opal Coast, North of France. In this regard, one of the topics of the multidisciplinary, state funded, project MARCO (Recherche marine et littorale en Côte d’Opale) is the highly space and time resolved study of sea state in the English Channel.As part of MARCO, this study has been focused on Global Satellite Navigation Systems Reflectometry (GNSS-R), a bistatic radar technique that uses the signals broadcasted by GNSS satellites as signals of opportunity. A GNSS-R receiver analyzes the signals reaching the receiver directly as well as the signals previously reflected off the Earth surface. Remote sensing application of GNSS-R considers today properties of water bodies, land and ice surfaces. In this work, the objective is to retrieve sea state and related wind speed information from the analysis of direct and reflected GNSS signals.Several sets of GNSS front-end data have been recorded along the Opal Coast, between the cities of Calais and Boulogne-sur-Mer, between the 12th and 19th of July 2019. The signals were sensed by a dual polarization (Right-Handed and Left-Handed Circular Polarizations) antenna mounted on a gyrocopter. Four datasets of ~18min obtained at an altitude of ~780m above sea level at a speed of ~95 km/h are analyzed by studying the RHCP signals received from 9 GPS satellites for each flight. Considering the altitude of the copter, the major axis of the observed first Fresnel zone is of 25m, 70m and 950m for respective satellite elevation angles of 85° (maximum observed), 30° (regular) and 5° (minimum observed). The raw data is sampled at a frequency of 16.368MHz. The in-phase and quadrature components, for both the direct and reflected signals, are obtained at a rate of 50 Hz. The sea state dependent surface reflectivity is estimated every minute.The signals are processed using a software receiver by means of Delay, Phase and Frequency Locked tracking Loops (DLL, PLL, FLL), aided by a modeling of the difference between the direct and reflected paths for the DLL of the reflected signal. The phasors of the resulting in-phase and quadrature components of the reflected signal are analyzed in the spectral domain in order to determine their coherency and subsequently retrieve the sea state. A rough sea yields reflections from a large surface area, resulting in a non-coherent mixture of phasors and a spread peak in the reflected signal spectrum. A calm sea yields specular reflection from small surface area resulting in a spectrum with a sharp peak. Preliminary results show Pearson correlation coefficients between the spectral spread of the peak and ERA5 wind speeds of 0.61 (high elevations) to 0.94 (low elevations).An important contribution of the airborne GNSS-R system applied in this work is the high spatial resolution of the data. The main perspective of this work is to further improve its time resolution, up to 50Hz.

This article is dedicated to the estimation of the GNSS signal carrier-to-noise ratio using the in-phase component of the signals as observations. In a GNSS receiver, it is the statistic of the correlation provided by the code tracking loop that is used to estimate the carrier-to-noise ratio. In fact, carrier-to-noise estimation is used to monitor the performance of GNSS receivers and the quality of the received signals. In this article, we aim at high rate carrier-to-noise estimation, namely the code repetition rate (e.g. 1ms for GPS C/A), in order to maximize the time resolution of carrier-to-noise observations. We show that in a 1-bit quantization receiver, the in-phase component of the signal can provide a direct observation of the signal amplitude, and therefore of the carrier-to-noise ratio. However, the model that links the 1ms rate observations of the in-phase component with the signal amplitude is non-linear. The non-linear expression that links the maximum value of the in-phase correlation component to the signal amplitude is derived. In order to estimate the time varying amplitudes of the signals, we propose an Extended Kalman Filter to reverse the non-linear expression with the noisy observations of correlation provided by the tracking loop. The proposed model and filter inversion method are assessed on synthetic and real data, while investigating the effect of the cross-correlation contribution of the visible satellites on the estimations. We show using real data that, for a 1-bit quantization receiver, the proposed estimator can achieve the same accuracy as a widely known commercial GNSS receiver with a much higher data rate. We also show that the proposed approach can cope with abrupt changes in the observations compared to a classical C / N 0 estimate.

The problem of coherent Doppler frequency estimation is addressed here applied to the domain of active hydroacoustics in a context of high Signal to Noise ratios. Pulse-to-pulse coherent Doppler sonars allow non-intrusive high-resolution velocity measurements that are often delicate to analyse, due to the nature of the return signal. We present here a novel pulse-to-pulse coherent Doppler estimator using signal processing techniques defined in the circular domain aiming to better reconstruct the noisy phase of the Doppler signal at high temporal resolutions. This Linear Circular estimator was tested on hydroacoustics synthetic measurements and its robustness to noise was demonstrated compared to the standard pulse-pair algorithm. The study suggests its high potential for time-resolved quasi-instantaneous velocity estimation, and turbulence statistics evaluation. The Linear Circular estimator's robustness to noise also suggests potential interests in applying such methods for incoherent scattering evaluation.

This article is dedicated to the estimation of the parameters of a linear-circular regression model. For this model, the response is circular and defined between −π and π, the predictor is linear and several sensors provide noisy observations of the response. In our approach, the noise is assumed to be distributed according to a von Mises distribution with a concentration parameter that models the accuracy of the sensors. We propose a maximum likelihood circular fusion operator for the estimation of the intercept, the slope of the regression line and the concentration parameter associated with each sensor. The proposed estimate is not direct as in the linear case and requires an iterative algorithm to maximize a periodic contrast function. In order to characterize the accuracy of our fusion operator, the theoretical expression of the variance of the proposed estimator slope is first derived. For this derivation, we approximate the von Mises distribution by a Wrapped normal distribution and we consider unwrapped observations. Then, we derive an iterative procedure to maximize the contrast function. We show, using synthetic data, that the variance of the slope of the regression line derived using the proposed estimate is in good agreement with that obtained using the theoretical expression of the variance. The proposed estimator is also used to process the carrier-phase difference between GNSS signals provided by two antennas. The objective in terms of signal processing is to estimate the linear parameters of this difference in order to derive the height between the two antennas. We show that fusing the observations provided by several satellite signals improves the accuracy of the estimated height. We also show, using real data, that the theoretical study of the proposed estimator can be used to predict the length of integration of the signal necessary for obtaining an estimate of the height with a given accuracy.

Navigation in constrained areas such as ports or dense urban environments is often exposed to global navigation satellite system (GNSS) satellites masking caused by the infrastructures. In this case, the GNSS positioning is inaccurate or unavailable and proprioceptive sensors are generally used to temporarily localise the vehicle on a map. However, the drift of these sensors rapidly causes the navigation system to fail. In this study, the navigation is computed using magnetometer and GNSS observations defined in an absolute reference frame. The heading measurements are coupled with a digital map of the road network in order to localise the vehicle in a map matching process. The contribution of this study is the proposition of a particle filter that fuses the position and direction observations to estimate the vehicle position. In this context, when the GNSS signal is masked, the observations of direction are used to compute the vehicle position. The proposed filter is defined in both circular and linear domains in order to take into account the nature of the observations. The proposed approach is assessed on real and synthetic data.

One of the main problem in maneuver detection in angle-only observation applications is the model change during the maneuver that creates a peak in the estimate error. We propose in this paper to estimate a vehicle’s heading using a Circular IMM defined in the bayesian framework and using the von Mises distribution. The performances of this novel filter is compared with a circular filter and a classical Kalman Filter in a Y shape road.

This study concerns the implementation of an underwater positioning system in shallow water environment. Shallow water localization of the beacon is done by using the propagation of acoustic signals in water. The experiment is based on the use of a boat equipped with two hydrophones and an emitting beacon. The signal received on each sensors are processed to compute the difference in time arrival between them (TDOA) and to derive the boat location. In this paper a close loop architecture is proposed to process the signal. This architecture based on a Kalman filter allows to track the peaks of correlation in a delay Doppler map. Underwater tests are done to validate the method. They lead to a precision of about one meter.

In this article, we propose a circular change-point detector for on-line processing of the phase and the frequency of a GPS-L1 signal. The aims of this processing are to get an accurate estimation of the phase and to use it to get centimeter precise position estimates every millisecond. We propose to track the phase of the GPS signal in an open loop and the frequency in a semi-open loop. In an open loop, the phase delay evolves as a circular random variable. Furthermore, the phase is subject to cycle slips. These abrupt changes must be detected and repaired. We propose a circular generalized likelihood test for the on-line detection of changes in the phase measurements. With the estimation and detection being non-linear, we propose a particle filter defined according to the circular von Mises distribution for the estimation of the phase and frequency. The proposed architecture is assessed using synthetic and real data.

This article presents the implementation of an indoor localization approach that combines map matching and a circular particle filter defined in a Bayesian framework. The technique relies only on velocity and heading observations coupled with a map of the road network. No prior knowledge of the initial position is given. A circular distribution is used to match the vehicle's heading with the roads direction. This allows to detect turns and provide a more accurate position estimate. The algorithm is assessed with a synthetic dataset in a real context.

GNSS-R techniques use direct and reflected signals coming from GNSS satellites to characterize the reflecting surface. In altimetry applications, the height of the surface is derived from the path difference between these two signals. The aim of this article is to propose a receiver architecture for high rate estimation of this path difference using an Interference Pattern Technique. An original receiver front-end architecture is presented, aiming at providing noisy observations of a non-linear function of the height. In order to invert this non-linear observation function and to smooth the height estimation, a particle filter is used. The proposed approach is assessed on synthetic data.

We present in this article a Bayesian estimation method for the joint segmentation of a set of piecewise stationary processes. The estimate we propose is based on the maximization of the posterior distribution of the change instants conditionally to the process parameter estimation. It is defined as a penalized contrast function with a first term related to the fit to the observation and a second term of penalty. The expression of the contrast function is deduced from the log-likelihood of the parametric distribution that models the statistic evolution of processes in the stationary segments. In the case of joint segmentation the penalty term is deduced from the prior law of change instants. It is composed of parameters that guide the number and the position of changes and of parameters that will bring prior information on the joint behavior of processes. This work is applied to the estimation of wind statistics parameters. We use data available from a cup anemometer and a wind vane, supposed to be piecewise stationary. The contrast function is deduced from the circular Von Mises distribution for the wind direction and from the log-normal distribution for the speed. The feasibility and the contribution of our method are shown on synthetic and real data.

The works presented in this paper deals with the design of a navigation system which is based on the fusion of information coming from a multi-frequencies GPS receiver, an odometer and a gyroscope. We present the design of a tracking fusion filter for the future multi-carrier G.P.S signal. This filter is an hybrid GPS/dead-reckoning fusion system that overcome the problem of reduced satellite visibility. In this system the estimation of the distance and speed between the satellite and the vehicle is realized with a Kalman track fusion algorithm. This algorithm is inspired from the modified track-to-track algorithm, the observations of pseudo distances are merged with a measurement fusion algorithm and the measurements of distances and speeds are fused with a state vector fusion Kalman filter. The simulation results obtained on synthetic data show the feasibility of the method and the contribution of the fusion over the accuracy and continuity of the localization.

The works presented in this paper deals with the design of a tracking fusion filter for the future multi-carrier G.P.S signal. The estimation of the distance and speed between the satellite and the G.P.S receiver is realized with a Kalman track fusion algorithm. In this system, inspired from the modified track-to-track algorithm, the observations are merged with a measurement fusion algorithm and the estimates are fused with a state vector fusion Kalman filter. This hybrid model can fuse the measurement obtained from the three frequencies of the future G.P.S. system. It can also fuse the state vectors that describe distances and speed which are supposed to come from different sensors. The simulation results obtained on synthetic data show the feasibility of the method and the contribution of the fusion over the localization accuracy.

Global Navigation Satellite Systems signals can be used in bi-static radar systems in order to get altimetric measurements. With a single classic GNSS ground-based receiver processing the combination of the signals coming directly and after one reflection to the receiving antennas, the Interference Pattern Technique allows the computation of the height of the reflecting surface. In this case, the observed parameter is the Signal to Noise Ratio of the received composite signal, which oscillates at a frequency proportional to this height. However, the signal recordings are generally very long since the accuracy of the SNR frequency estimation is proportional to the variation of the satellite elevation during the observation interval. In this article, we propose a calibration technique that allows reducing the observation duration while keeping a centimeter accuracy performance. The proposed technique is tested on both synthetic and real data.

It is well known that reflected signals from Global Navigation Satellite Systems (GNSS) can be used for altimetry applications, such as monitoring of water levels and determining snow height. Due to the interference of these reflected signals and the motion of satellites in space, the signal-to-noise ratio (SNR) measured at the receiver slowly oscillates. The oscillation rate is proportional to the change in the propagation path difference between the direct and reflected signals, which depends on the satellite elevation angle. Assuming a known receiver position, it is possible to compute the distance between the antenna and the surface of reflection from the measured oscillation rate. This technique is usually known as the interference pattern technique (IPT). In this paper, we propose to normalize the measurements in order to derive an alternative model of the SNR variations. From this model, we define a maximum likelihood estimate of the antenna height that reduces the estimation time to a fraction of one period of the SNR variation. We also derive the Cramer–Rao lower bound for the IPT and use it to assess the sensitivity of different parameters to the estimation of the antenna height. Finally, we propose an experimental framework, and we use it to assess our approach with real GPS L1 C/A signals.

Map matching is the process of finding a match for each GPS point in a vehicle's trajectory to roads on a digital map. Extensive research has been conducted during the last years yielding many algorithms based on different approaches. One of the challenges that face those algorithms is the interruption of GPS signals that occurs specially in dense urban environments. In these cases on-board sensors like odometers and accelerometers can be used temporarily for positioning, however due to the poor accuracy of these methods, the quality of map matching decreases significantly. In this paper, we propose a method that improves the quality of map matching when GPS signals are not available. This method is based on a particle filter using heading and velocity measurement. We evaluate this method through its integration with an existing topological map matching algorithm. We compare the performances when this algorithm is used alone and when associated with the particle filter.

Soil moisture can be processed with the difference of power between a direct and a nadir GNSS signal reflected by the soil. The estimate carrier to noise ratio C/N 0 is a classical measure of power for the GNSS signals. We show in this paper that the classical estimate of C/N 0 processed for one satellite depends on the carriers, code and phase delay of the visible satellites. This dependence decreases the reproducibility of the measurements. In this paper we propose a non-linear model that links the power of the satellites with the observations of correlation obtained for the in phase component of the prompt local code. This expression takes into account the parameters of the GNSS signal and allows to improve the reproducibility of measurements. Unfortunately the expression is non linear and a particle filter is used to recover the signals amplitudes from the observations of correlation. In the experimentation the proposed approach is assessed on synthetic signals in the dynamic and static case.

This paper proposes circular data processing tools dedicated to the tracking of the phase of GNSS signals in a Phase Open Loop, particularly in case of multi-channel signal structure. The objective of processing the phase in an open loop is to avoid time-correlation between two successive measurements. This allows the use of loop filters in order to smooth the measurements without producing unwanted oscillations in the phase estimations. In order to process the angular values produced by the Phase Open Loop, the choice had been made to develop a filter and a fusion operator in a Bayesian framework with circular statistics distributions. The proposed tools are assessed on synthetic and real data.

New GNSS signals broadcast at different carrier frequencies include, most of them, a pilot and data channels. In this article we propose to fuse the information of code and phase delay provided by the two channels in order to improve the position accuracy and to extract the message of navigation. The proposed system is composed of a change point estimate of the slope of discriminator variations and of a circular recursive filter for the estimation and fusion of the carrier phase delay. The contribution of this work is the definition of a recursive change point estimate that fuse the discriminator values and integrate prior information for the detection. The proposed architecture that uses a circular filter enables to fuse the phase delays and to extract the message of navigation. The method is assessed on synthetic data in the experimentation.

Many practical application in the field of robotic and perception are using angular data. In this work we present a multi-sensor multi-temporal data fusion filter for angular data. Most of the time, statistic filters, are designed on linear domain. In this work we propose a recursive filter defined on the circular domain with a von Mises distribution. In our application we consider a set of measurement taking at different instants and provided by different sensors. The sequential implementation of the recursive fusion filter we propose is deduced from the a posteriori distribution of the state vector, containing the angular direction and velocity. Temporal measurements are coming from several sensors. The feasibility and the contribution of our method are shown on synthetic data.

Mean wind speeds and mean wind directions are important input parameters for environmental models and applications. In many practical cases, anemometer sensor provides measurements of direction and wind speed, supposed to be Gaussian and piecewise stationary. In this work we use a von Mises distribution and a Log-normal distribution in order to model sensor data with distributions having a physical reality. We derive from these distributions an “on-line” recursive estimate of the change point in the wind speed and direction processes. We present numerical results obtained using synthetic and real data to demonstrate the ability of these estimates.

In this work we investigate the potential for sensing beach soil moisture with the L band GPS bistatic radar concept. Characterisation of sediment surface (properties like humidity) is indeed important to better understand morphodynamic activity of intertidal part of beaches. In our approach we compare the direct GPS Signal to Noise Ratio with the reflected one in order to measure the soil moisture. We use a bit grabber to digitize and store the GPS L1 carrier (1.5 Ghz) samples. In this context the signal processing is off-line. In this work we proposed a model of the received signal after demodulation and demultiplexing. We deduce from this model a MAP estimate of the navigation message and of the signal SNR. In our case the signal model is a piecewise stationary process with change instants at bit edge position. We present preliminary SNR measurement with this technique for the discrimination of water and humid sand.

This paper presents a Bayesian off-line fusion segmentation method, applied to the code tracking in a multi-carrier GPS receiver. The tracking is realized with discriminator values obtained on the different carrier frequencies. We suppose that the evolution of the pseudo-ranges satellites-receiver is piecewise linear. We propose a Bayesian method for the fusion of change detection models in the discriminators evolution. In this context, we construct a penalized contrast function to estimate the model parameters. The contrast function is deduced from log-likelihood of the parametric distribution that models the discriminators statistic evolution. We deduced the penalty term from the prior law of change instants. It is composed of parameters that guide the number of changes and of parameters that will bring prior information on the ionospheric delays between the GPS signals on the different carrier frequencies. We show on synthetic and real data the feasibility and the contribution of our method.

We present in this article an off-line Bayesian estimation method for the segmentation of piecewise linear processes. The estimation is defined as a penalized contrast function with a first term related to the observations and a term of penalty deduced from the prior law of change instants. This technique is used for the distance estimation between a satellite and a navigation receiver. The distance determination is performed by the delay measure of a signal propagation time emitted by the satellite. For the delay evaluation, a discriminator signal is calculated from the correlation between a code contained in the signal and the same code generated by the receiver at the same instant. The method we propose is applied to these discriminator measurements. It makes possible to precisely reconstitute the delay evolution rather than providing it sequentially. Our work's contribution is shown on synthetic and real data.

The subject of this article is about study and realisation of a tight hybrid filter for vehicle positioning in dense urban area. In our approach we fused the measurements coming from the sensors in a hybrid filter at the pseudo-ranges level. The proposed filter combines the "observables" (measurements) of phase and code of a GPS receiver with the measurements of an odometer and a gyrometer. We describe in this article the design of the fusion filter inspired of the distributed "track to track" Kalman filter architecture. In our approach the pseudoanges rspredictions, construct with the phase measurements of the GPS and the Dead-reckoning measurements system, are fused. The fused predictions are them corrected with the GPS code measurements of the pseudo-ranges. We use the classical snapshot least squares estimation technique to calculate the position with the estimated pseudo-ranges. We present in this article different simulations results obtained in a real dense urban context. We show that the tight hybrid filter we propose gives a location at least as precise as the classical loosely coupled hybrid filter. This result is obtained for a number of satellites lower than four and various values of the HDOP (Horizontal Dilution Off Precision).

In this article we describe a new approach of brooking stationary detection in a noisy signal. We consider the signal corrupted by an additive stationary noise, whose form of the density of probability is none. This new approach leans on the detection and the analysis of the transitions of the signal. The extraction of transition in the signal associates, to each sample in the transition, a constant value equal to the surface of the signal in the transition. The application of this operator to the gradient of the signal gives us the value of the transitions dynamics in the signal. The dynamics transitions density probability modelization by a parametric curve allows us to deduce the level of noise in the signal. It is from the level of noise that we determine a threshold on the height of the transitions. Then we consider the transitions smaller than the threshold as transitions of the noise and the transitions higher than the threshold as the variations of the signal without noise. THis technique of rupture detection is entirely automatic and self adapted to the level of noise in the signal. We present the study and the implementation of this global approach for the detection of roof and step edges in range images. We detect roof and step edge in the image with the signals of transition extracted in two orthogonal directions.

The long-term neuropsychological and psychiatric sequelae of herpes simplex virus encephalitis (HSVE) and their relationship to the volume of temporal lesions and to amygdala and hippocampus damage remain undefined. We have conducted a prospective study of long-term sequelae in 11 patients with clinically presumed HSVE and detection of HSV DNA in the cerebrospinal fluid by polymerase chain reaction. Six months after encephalitis, patients underwent neuropsychological and language assessment. At the same stage, single photon emission computed tomography (SPECT) evaluated the occurrence of hypoperfusion with an index of asymmetry. MRI was used for the measurement of amygdala, hippocampus and cerebral lesions by two blind neurologists. The volume of the amygdala and hippocampus was compared with those of five controls, matched for age and level of education. Long-term memory disorders were seen in 6 patients, associated with the larger lesions and damage of at least two structures. Long-term behavioural changes with emotionalism, irritability, anxiety or depression were prominent in 7. Left prefrontal hypoperfusion appeared in 8 patients, associated with psychiatric disorders in 7 and left amygdala damage in 6. The reduction of amygdala and hippocampus volume was correlated with the overall volume of lesions. Different patterns of mesial temporal lobe damage occurred, involving either amygdala alone, or amygdala and hippocampus, but never hippocampus alone. MRI volumetric measurements in HSVE could be a good indicator of long-term prognosis. Persistant behavioural changes could be related to an amygdala and frontal dysfunction.

The problem of the choice of slices angles, at the time of diagnosis of brain fetal malformations, is linked to the position of the fetus inside the uterus. The 3D reconstruction of intern parts of the brain and especially the callosus corpus can help to detect some malformations. This kind of reconstruction pass by several steps that depend all on the initial segmentation step. The main difficulties of the segmentation are linked on the one hand to the inherent noise of ultrasound imaging and on the other hand to the matching of views of the 2D sequence to process. The 3D reconstruction stage require the definition of a marker in the sequence of process. In agreement with physicians, we have used the cranial contour as reference on the one hand because it is considered as invariable and fixed and on the other hand because of its more pronounced contrast (due to the fact of its cartilaginous nature) than the other structures. Nevertheless, the classic techniques of segmentations have remained without effect (open contour, too noisy). Therefore, we have developed an algorithm allowing to define automatically the ellipse. This method is based on a parametrically deformable model using elliptic FOURIER decomposition.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A new method for automatically reconstructing a three dimensional object from a serial cross section is presented in this paper. The method combines the technical chaining contour and the sampling method to construct an object. In the proposed method the initial description of the object is formed by a serial continuous contour. First we sample each cross section to determine the nods that constitute the vertices of the triangles used for the reconstruction, then we determine the tangent in these nods and we assign to each one of them a code depending on the tangent direction. For triangulation there is nothing left to do but collect the nods of two adjacent contours the code of which is identical.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

**J,p PRUVO, ***Jp DUBUS* Laboratoire instrumentation du signal, de l'image et des reseaux de Calais*** Laboratoire de mesure automatique de Lille** Laboratoire de neuroradiologie de LilleKEY WORDS : segmentation, modelization, volumic quantification, Nuclear Magnetic Resonance Imaging.SUMMARYWe are suggesting a new approach to NMR image segmentation for multiple scleroses volume quantification. Thechoice of a segmentation technique is conditioned by whatis known in NMR acquisition artefacts. This research has beenconducted in collaboration with the Lille Neuroradiology Laboratory leading to the implementation of a disease developmentindex.

Several regional augmentation GNSS systems (like SBAS) are already fully operational and other are under development with new frequencies and signals on the way. Therefore, it becomes imperative to guarantee for all GNSS users the compatibility, interoperability and interchangeability between all these systems. The goal is to ensure that the user's multi-mode receiver can choose and mix signals from different GNSS and SBAS systems to achieve more availability, accuracy and robustness. Attaining that objective will require agreements on frequency plans and signal designs, as well as other details including means to ensure interoperability of system times and geodetic reference systems. This paper suggests a Universal-SBAS (U-SBAS) standard, compatible with all the existing and planned regional GNSS systems (and their evolutions) in the world, like IRNSS, QZSS, PCW, BEIDOU-1, WAAS, EGNOS, SDCM, GAGAN, MSAS. The proposed worldwide multimodal U-SBAS standard carries additional channels (signals and messages) to cover the non-aeronautical specific Safety-of-Life (SoL) services, and also High Precision Positioning Services (HPPS), Position Velocity Time (PVT), authentication services, safety services, scientific application services, High Precision Timing Services (HPTS), etc. U-SBAS is designed to be fully interoperable with the current SBAS standards and to allow significant performance and service improvements in operational, scientific and/or security areas. Finally usage examples of the proposed standard are given for different types of applications such as science, aviation, precise point positioning, timing, security, robust positioning, maritime and reflectometry.

The works presented in this paper deals with the design of a tracking fusion filter for the future multi-carrier G.P.S signal. The estimation of the distance and speed between the satellite and the G.P.S receiver is realised with a Kalman track fusion algorithm. In this system, inspired from the modified track-to-track algorithm, the observations of the same type are merged with a measurement fusion algorithm and the measurements of different types are fused with a state vector fusion Kalman filter. This hybrid model can fuse the measurement obtained from the three frequencies of the future G.P.S. system. It can also fuse the state vectors that describe distances and speed which are supposed to come from different sensors. The simulation result obtained on synthetic data show the feasibility of the method and the contribution of the fusion over the accuracy of the localization.

When a value is observed by a temporal series of periodic measurements, such as angle measurements, the statistic tools of the linear domain are no longer adapted to the estimation of the statistical parameters of this series. We propose several filters and a fusion operator adapted to angular random variables estimation. They are developed in a Bayesian framework with the von Mises circular normal distribution. Moreover, we propose a Global Likelihood Ratio circular change estimator which relies on a particle filter defined in the circular domain. GNSS signal procedding traditionnaly relies on the recursive estimation of three parameters in lock loops : the code delay, the phase and the frequency of the received signal carrier. We show in this thesis that the phase and frequency estimations, when realized in a lock loop, cause imprecisions and additional perturbations. We proposea new approach for the estimation of these parameters in an open loop. in this context, the phase measurements are angles defined between ‒π et π. They can be modeled using the von Mises distribution, thus we use the developed circular estimation tools in order to process them. We show the benefit of the proposed approach for positioning and for height estimation using the GNSS-Reflectometry technique. The obtained precision is of the centimeter order, for integration times of one millisecond. We also propose a phase ambiguity resolution technique which can be used in a mono-receiver , mono-frequency approach.; Lorsqu'une grandeur est observée par une série temporelle de mesures périodiques, telles que des mesures d'angles, les outils statistiques du domaine linéaire ne sont plus adaptés à l'estimation des paramètres statistiques de cette série. Nous proposons des filtres et un opérateur de fusion adaptés aux grandeurs aléatoires circulaires. Ils sont développés dans un cadre bayésien avec la distribution circulaire normale de von Mises. De plus, on propose un estimateur circulaire GLR (Global Likehood Ratio) de ruptures dans un signal angulaire s'appuyant sur un filtre particulaire défini dans le domaine circulaire. Le traitement des signaux GNSS repose traditionnellement sur l'estimation récursive, en boucles fermées, de trois paramètres : le délai de code, la phase et la fréquence porteuse du signal reçu. On montre dans cette thèse que l'estimation de la phase et de la fréquence en boucle fermée est à l'origine d'imprécisions et de perturbations additionnelles. On propose une nouvelle approche pour l'estimation de ces paramètres en boule ouverte. Dans ce cas, les mesures de phase sont des angles, définis entre ‒π et π. Statistiquement, elles peuvent être modélisées par la distribution circulaire de von Mises et l'on utilise donc les outils d'estimation circulaire développés dans cette thèse pour les traiter. On montre l'intérêt de l'approche proposée pour le positionnement et pour l'altimétrie par réflectométrie des signaux GNSS. La précision obtenue est de l'ordre du centimètre, pour des durées d'intégrationd'une milliseconde. On propose également une technique de résolution de l'ambiguïté de phase, utilisable dans une approche mono-récepteur et mono-fréquence.