Juliette Marais

Land mobile GNSS availability and multipath evaluation tool

Applications of global navigation satellite system (GNSS) in land transportation systems are already extensively deployed and will certainly continue to grow especially in the framework of intelligent transport systems. However, one of the best-known drawbacks of such a system is the lack of satellite visibility in dense urban areas as well as in some specific embedded railway environments. This restricts considerably GNSS use for extended safety related applications. In this paper, a new tool is proposed to predict the availability of a satellite constellation from the point of view of the land transportation user. Knowing the trajectory of a land vehicle, the tool predicts the number of satellites that will be received and produces a safety criterion able to qualify the GNSS localization result. A first version of the tool, already in operation, merges an image processing approach providing the knowledge of the land environment, and the output of a satellite tracking program predicting satellite positions in the sky. This allows us to determine, using a simple optical approach, the number of satellites received in line-of-sight or blocked, with regard to the nearby environment of the receiving antenna. Results obtained in railway as well as in road environments show that satellite signals received by multipath are often used by GNSS receivers in the localization process. Thus, propagation characteristics of the satellite signals in an urban canyon configuration were characterized to determine when a signal received by reflected ray is used by the receiver or not. A criterion related to the satellite elevation is defined to improve the overall performance of the predictive tool. Comparisons with real measurements are commented on. Both simulations and measurements are very similar.

Gnss performance enhancement in urban environment based on pseudo-range error model

Today, GNSS (Global Navigation Satellite System) systems made their entrance in the transport field through applications such as monitoring of containers or fleet management. These applications do not necessarily request a high availability, integrity and accuracy of the positioning system. For safety applications (for instance management of level crossing), the performances require to be more stringent. Moreover all these transport applications are used in dense urban or sub-urban areas, resulting in signal propagation variations. This increases difficulty of getting the best reception conditions for each available satellite signal. The consequences of environmental obstructions are unavailability of the service and multipath reception that degrades in particular the accuracy of the positioning. Our works consist in two main tasks. The first one concerns the pseudo-range error model. Indeed, the model differs in relation of the satellite state of reception. When the state of reception is direct, as described in literature, the associated pseudo-range error model is a Gaussian distribution. However, when the state of reception is NLOS (Non Line Of Sight), this assumption is no more valid. We have shown that the associated model can be approximated by a Gaussian mixture. The Second contribution concerns the reception state evolution. We have modeled the propagation channel with a Markov chain. From the state of reception of each satellite, we deduce the appropriated error model. This model is then used in a filtering process to estimate the position. The approach is based on filtering methodology and on the application of a Jump Markov System algorithm.

Dirichlet Process Mixtures for Density Estimation in Dynamic Nonlinear Modeling: Application to GPS Positioning in Urban Canyons

In global positioning systems (GPS), classical localization algorithms assume, when the signal is received from the satellite in line-of-sight (LOS) environment, that the pseudorange error distribution is Gaussian. Such assumption is in some way very restrictive since a random error in the pseudorange measure with an unknown distribution form is always induced in constrained environments especially in urban canyons due to multipath/masking effects. In order to ensure high accuracy positioning, a good estimation of the observation error in these cases is required. To address this, an attractive flexible Bayesian nonparametric noise model based on Dirichlet process mixtures (DPM) is introduced. Since the considered positioning problem involves elements of non-Gaussianity and nonlinearity and besides, it should be processed on-line, the suitability of the proposed modeling scheme in a joint state/parameter estimation problem is handled by an efficient Rao-Blackwellized particle filter (RBPF). Our approach is illustrated on a data analysis task dealing with joint estimation of vehicles positions and pseudorange errors in a global navigation satellite system (GNSS)-based localization context where the GPS information may be inaccurate because of hard reception conditions.

Method for evaluating an extended Fault Tree to analyse the dependability of complex systems: Application to a satellite-based railway system

Evaluating dependability of complex systems requires the evolution of the system states over time to be analysed. The problem is to develop modelling approaches that take adequately the evolution of the different operating and failed states of the system components into account. The Fault Tree (FT) is a well-known method that efficiently analyse the failure causes of a system and serves for reliability and availability evaluations. As FT is not adapted to dynamic systems with repairable multi-state components, extensions of FT (eFT) have been developed. However efficient quantitative evaluation processes of eFT are missing. Petri nets have the advantage of allowing such evaluation but their construction is difficult to manage and their simulation performances are unsatisfactory. Therefore, we propose in this paper a new powerful process to analyse quantitatively eFT. This is based on the use of PN method, which relies on the failed states highlighted by the eFT, combined with a new analytical modelling approach for critical events that depend on time duration. The performances of the new process are demonstrated through a theoretical example of eFT and the practical use of the method is shown on a satellite-based railway system.

GNSS accuracy enhancement based on pseudo range error estimation in an urban propagation environment

For new ITS applications, positioning solutions will require to be more accurate and available. The most common technique used today is composed of a GPS receiver, sometimes aided by other sensors. GPS, and GNSS in general, suffer from masking effects and propagation disturbances in urban areas that cause biases on pseudo range measurements. Mitigation solutions sometimes propose to detect and exclude outliers but in land transportation applications, such a decision reduces dramatically the service availability and thus, the interest of satellite-based solutions. In order to optimize the use the satellites received, we propose a new positioning algorithm based on signals only with pseudo range error modeling in association with an adapted filtering process. The model and the filter have been validated with simulation data performed along an urban bus line and have shown that both positioning error and availability can be improved. Along the trajectory tested, the mean accuracy has been reduced from 5.3m with a classical filter to 2.6m with our algorithm with 89% of the points more accurate than 5m instead of 64% before.

Evaluation of GPS availability for train positioning along a railway line

The use of localisation systems in transport is ever present, particularly with respect to satellite systems. However, studies show that the performance of satellite based localisation processes are often degraded in high-rise masked environments such as railway environments. Experiments have shown that high obstacles bordering a railway line fundamentally characterise the availability of the service. The paper presents our tool based on image processing and software, which predicts the satellites paths. It determines whether the satellites can be received or not with regard to the nearby environment. It presents the characteristics of propagation effects that are defined and included in the tool. The experimental and computation results are also presented.

Counting of satellites with direct GNSS signals using Fisheye camera: A comparison of clustering algorithms

This paper investigates the problem of accuracy of localization with GNSS in constraint environments. The ultimate goal is to provide a first confidence index on the accuracy of the position given by the GNSS. In this paper, we propose to use the complementarity between the GNSS signals and the development in image processing to count satellites with direct reception state. It consists to use a vehicle equipped with a GPS-RTK and a camera oriented upwards to capture images and count after repositioning, the satellites with direct signals (resp. with blocked/reflected signals) i.e. located in the sky region of the image (resp. located in the not-sky region). The proposed approach is based on an optimal clustering applied on simplified images. More preciously, the acquired image is simplified using a geodesic reconstruction with an optimal contrast parameter. Then, a clustering step is made in order to classify the regions into two classes (sky and not-sky). For that, a set of unsupervised (KMlocal, Fuzzy C-means, Fisher and Statistical region Merging) and supervised (Bayes, K-Nearest Neighbor and Support Vector Machine) clustering algorithms are compared in order to define the best classifier in terms of good classification rate and processing time. Experimental results are shown for hundred images taken in different conditions of acquisition (illumination changes, clouds, sun, tunnels, etc).

Quantification of GNSS signals accuracy: An image segmentation method for estimating the percentage of sky

This paper is focused on the characterisation of the GNSS reception signals environment by estimating the percentage of visible sky. The estimation is based on a new segmentation technique that uses color and texture gradients with an adaptive and non-parametric combination strategy. The structural gradient, resulting from the combination, is processed with the watershed algorithm to get image segmentation. The classification process used to extract the sky region is performed using the k-means algorithm. Experimental segmentation and classification results, using real data and an evaluation methodology, are presented to demonstrate the effectiveness and the reliability of the proposed approach.

A Survey of GNSS-Based Research and Developments for the European Railway Signaling

Railways have already introduced satellite-based localization systems for non-safety related applications. Driven by economic reasons, the use of these systems for new services and, in particular, their introduction in signaling system is seriously investigated today and tested all around the world. Because of the weight of their history, their strong normative context, and the high requested level of safety, the introduction is relatively slow. The aim of this paper is to provide a survey of past and current programs dealing with global navigation satellite systems as a basis to introduce main issues relative to context, standards, performance requirements, and safety proofs. Links with aeronautical concepts are also presented, illustrating the transposable principles and the limits due to the land transport environment.

Image analysis based real time detection of satellites reception state

This paper is focused on real time detection of satellite reception state. In constrained environment, such as urban areas, GNSS signals can be received directly, reflected or blocked by obstacles (building, vegetation, etc) and can lead to an error or a lack of positioning. This paper proposes to characterize the GNSS signals reception environment using image analysis. The proposed approach consists of detecting the visible sky part in the images thanks to a k-means based classification algorithm applied in segmented/simplified images. In this paper, two images segmentation/simplification techniques are proposed and compared in terms of robustness, reliability and processing rate. The first one is based on a combination of color and texture information. The second technique uses a geodesic reconstruction by dilatation with an optimal contrast parameter. The main aim of this work consists of determining how many satellites are positioning in the area of visible sky and have a direct reception state. Experimental classification and positioning results, using real data and an evaluation methodology are presented to demonstrate the effectiveness and the reliability of the proposed approach.