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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.

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.

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.

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.

Classical GPS/Acoustic positioning techniques using ranging position location in low depth environment suffer from the bad system geometry. The aim of this article is to propose a new hyperbolic position location technique that takes into account the system geometry. In our approach we process the position with the normal distribution of the beacon location defined as a sum of uniform distributions. A uniform distribution is derived from the surface of possible beacon localizations obtained with approximations of the hyperbolic approach in the case of noisy observations and for two boat positions. These surfaces increase when the geometry of the system is degraded and the probability in these cases decreases. These lower probabilities allow to minimize the influence of the bad geometry configuration in the finale estimation process. The proposed method is assessed on synthetic data and compared to the classical ranging approach.

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.

A hierarchical Bayesian model is applied to off-line segmentation of the GPS signal discriminator. The purpose of this work is to estimate the code delay of the receiving GPS CDMA code in order to retime the local receiver code and to estimate the pseudorange satellite receiver. The goal of our approach is to obtain a high-rate accurate positioning in the dynamic navigation case. We show that the behaviour of the coherent discriminator of a GPS pilot channel can be modelized by a piecewise stationary process. In our approach the discriminator behaviour in each stationary segment is approximated by a constant acceleration model, and the code delay at each end of the segments is known. The interest of this approach is that we use the coherent values of the discriminator in each segment to estimate the change instants of the process and to get in this case an accurate estimation of the code delays. In this context, a simultaneous estimation of the change instants is considered. We define the a posteriori distribution which integrates in its expression the signal change instants and the parameters of its statistical model. The proposed model leads after marginalization to a penalized contrast function that we minimize to estimate the discriminator change instants. The interest of the proposed model is that we can integrate in our estimate prior information on the roughly known values of the signal-to-noise ratio and relative speed satellite receiver. The potential of the proposed method is shown on experimentations realized on synthetic and real data for millisecond receiver localization.

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.

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 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.