A robust multisensors and multiple model localisation system

De nombreux travaux de recherches sont menés depuis quelques années dans le but de fournir une solution précise et intègre au problème de la localisation de véhicules routiers. Ces recherches sont en majorité fondées sur la théorie probabiliste de l’estimation. Elles utilisent la fusion multi-capteurs et le filtrage de Kalman mono-modèle, au travers de variantes adaptées aux systèmes non linéaires ; l’unique modèle complexe étant supposé décrire toute la dynamique du véhicule. Nous proposons dans cette thèse une approche multi-modèles. Cette étude dérive d’une analyse modulaire de la dynamique du véhicule, c’est-à-dire que l’espace d’évolution est pris comme un espace discret : plusieurs modèles simples et dédiés chacun à une manœuvre particulière sont générés, ce qui améliore la robustesse face aux défauts de modélisation du système. Il s’agit d’une variante de l’algorithme IMM, qui prend en compte l’asynchronisme des capteurs embarqués dans le processus d’estimation de l’état du véhicule. Pour cela, une nouvelle modélisation sous contraintes est développée, ce qui permet de mettre à jour la vraisemblance des modèles intégrés même en l’absence de mesures provenant de capteurs extéroceptifs. Toutefois, la performance d’un tel système nécessite d’utiliser des données capteurs de bonne qualité. Plusieurs opérations sont présentées, illustrant la correction du biais des capteurs, des bruits de mesures ainsi que la prise en compte de l’angle de dévers de la chaussée. La méthodologie développée est validée à travers une comparaison avec les algorithmes de fusion probabilistes EKF, UKF, DD1, DD2 et le filtrage particulaire. Cette comparaison est fondée sur des mesures courantes de précision et de confiance, puis sur l’utilisation de critères statistiques de consistance et de crédibilité, à partir de scénarios synthétiques et ensuite des données réelles.Many research works have been devoted in the last years in order to provide an accurate and high integrity solution to the problem outdoor vehicles localization. These research efforts are mainly based on the probability estimation theory. They use multi-sensor fusion approach and a single-model based Kalman filtering, through some variants adapted to nonlinear systems. The single complex model that is used is assumed to describe the dynamics of the vehicle. We rather propose a multiple model approach in this thesis. The presented study derives from a modular analysis of the dynamics of the vehicle, ie the evolution of the vehicle is considered as a discrete process, which combines several simple models. Each model is dedicated to a particular manoeuvre of the vehicle. This evolution space discretizing will improves the system robustness to modelling defects. Our approach is a variant of the IMM algorithm, which takes into account the asynchronism of the embedded sensors. In order to achieve this goal, a new system constrained modelling is developed, which allows to update the various models likelihood even in absence of exteroceptive sensors. However, the performance of such a system requires the use of good quality data. Several operations are presented, illustrating the corrections on the sensors bias, measurements noise and taking into account the road bank angle. The developed methodology is validated through a comparison with the probabilistic fusion algorithms EKF, UKF, DD1, DD2 and particle filtering. This comparison is based on measurements of accuracy and confidence, then the use of statistical consistency and credibility measures, from simulation scenarios and then real data

Localisation robuste multi-capteurs et multi-modèles

De nombreux travaux de recherches sont menés depuis quelques années dans le but de fournir une solution précise et intègre au problème de la localisation de véhicules routiers. Ces recherches sont en majorité fondées sur la théorie probabiliste de l estimation. Elles utilisent la fusion multi-capteurs et le filtrage de Kalman mono-modèle, au travers de variantes adaptées aux systèmes non linéaires; l unique modèle complexe étant supposé décrire toute la dynamique du véhicule. Nous proposons dans cette thèse une approche multi-modèles. Cette étude dérive d une analyse modulaire de la dynamique du véhicule, c est-à-dire que l espace d évolution est pris comme un espace discret : plusieurs modèles simples et dédiés chacun à une manœuvre particulière sont générés, ce qui améliore la robustesse face aux défauts de modélisation du système. Il s agit d une variante de l algorithme IMM, qui prend en compte l asynchronisme des capteurs embarqués dans le processus d estimation de l état du véhicule. Pour cela, une nouvelle modélisation sous contraintes est développée, ce qui permet de mettre à jour la vraisemblance des modèles intégrés même en l absence de mesures provenant de capteurs extéroceptifs. Toutefois, la performance d un tel système nécessite d utiliser des données capteurs de bonne qualité. Plusieurs opérations sont présentées, illustrant la correction du biais des capteurs, des bruits de mesures ainsi que la prise en compte de l angle de dévers de la chaussée. La méthodologie développée est validée à travers une comparaison avec les algorithmes de fusion probabilistes EKF, UKF, DD1, DD2 et le filtrage particulaire. Cette comparaison est fondée sur des mesures courantes de précision et de confiance, puis sur l utilisation de critères statistiques de consistance et de crédibilité, à partir de scénarios synthétiques et ensuite des données réelles.Many research works have been devoted in the last years in order to provide an accurate and high integrity solution to the problem outdoor vehicles localization. These research efforts are mainly based on the probability estimation theory. They use multi-sensor fusion approach and a single-model based Kalman filtering, through some variants adapted to nonlinear systems. The single complex model that is used is assumed to describe the dynamics of the vehicle. We rather propose a multiple model approach in this thesis. The presented study derives from a modular analysis of the dynamics of the vehicle, ie the evolution of the vehicle is considered as a discrete process, which combines several simple models. Each model is dedicated to a particular manoeuvre of the vehicle. This evolution space discretizing will improves the system robustness to modelling defects. Our approach is a variant of the IMM algorithm, which takes into account the asynchronism of the embedded sensors. In order to achieve this goal, a new system constrained modelling is developed, which allows to update the various models likelihood even in absence of exteroceptive sensors. However, the performance of such a system requires the use of good quality data. Several operations are presented, illustrating the corrections on the sensors bias, measurements noise and taking into account the road bank angle. The developed methodology is validated through a comparison with the probabilistic fusion algorithms EKF, UKF, DD1, DD2 and particle filtering. This comparison is based on measurements of accuracy and confidence, then the use of statistical consistency and credibility measures, from simulation scenarios and then real data.EVRY-Bib. électronique (912289901) / SudocSudocFranceF

Low cost sensors ego localization with IMM approach for unusual maneuvers

This paper presents the problematic of outdoor vehicle localization in unusual maneuvers under the IMM (interacting multiple model) approach. The IMM, contrary to the non modular methods, is based on the discretization of the vehicle evolution space into simple maneuvers, represented each by a simple dynamic model such as constant velocity or constant turning etc. This allows the method to be optimized for highly dynamic vehicles. In this work, we focus on the various vehicle dynamics identification in some special driving situations, including very strong accelerations, turning with a high speed (more than 15 mldrs-1) or backward driving with stops. The presented results are based on real measurements collected from different scenarios. These results show a real interest in using the IMM method in order to reach our goal

Improvement of the proprioceptive-sensors based EKF and IMM localization

This paper presents the localization problem of outdoor vehicles using Interacting Multiple Model (IMM) and Extended Kalman Filter (EKF), in their predictive step without exteroceptive sensors data. Usually, hybridization operates between exteroceptive sensors (e.g. GNSS) and proprioceptive sensors (e.g. Odometer, Inertial Measurement Unit etc.) through a merging algorithm. Common experiments use the GPS receiver PPS time for stamping the odometric, gyrometric and IMU measurements, after what all these sensors are in the same UTC reference time. Now it is well known that the low cost GNSS devices have a very low frequency compared to proprioceptive sensors, combined to a low accuracy. Therefore in order to assess the vehicle positioning at higher frequency for safety applications, the sensors measurements are generally synchronized before being exploited in the merging algorithm. In our approach, the sensors remain in their original frequencies. The objective is to design a reliable and robust system that exploits asynchronous data. In order to reach this goal it is important to guarantee accuracy and integrity of filters even during the predictive steps, when exteroceptive GNSS data are not available: that is proprioceptive-sensors based positioning. We introduce in this paper, a study on the influence of the road bank angle assessment on the output. This parameter is used to correct the gyrometric and inertial unit measurements leading to an improvement of both IMM and EKF predictive output positioning. Tests performed with real data proved the suitability of introducing this parameter in the system

A Reduced-Form Model for A Life Insurance’s Net Asset Value

International audienceIn this paper we develop a closed-form model for the net asset value of a life insurance portfolioaimed at simplifying the assessment and quantification of the impact of financial stress scenarioson the insurer’s solvency. In fact, using the current practice based on an internal model is timeconsuming and thus it is not relevant when it comes to carry out sensitivity studies that should require rapid action from the management. Due to the nature of the stress scenarios that are mostly related to the financial market determinants, their impact is quite straightforward on the market value of financial assets. Therefore, in this paper, we focus on the distortion caused on the liability side and investigate a reduced-form model for the best estimate liabilities that is not only easily interpretable but also capable of anticipating market variation impact in the liabilities. The model is built based on a dataset drawn from a French life insurer’s projection model using single, double and triple shocks on the interest rates yield curve, equity market value and profit sharing provision.In order to capture as much information as possible from the dataset, several feasible regression specifications are used. The general form of the empirical model is specified as a linear combination of the risk factors and its predictive ability is investigated based using an out-of-sample analysis

New likelihood updating for the IMM approach application to outdoor vehicles localization

This paper presents the problematic of outdoor vehicle localization under the IMM (interacting multiple model) approach. The IMM is now a well known modular approach, which is based on the discretization of the vehicle evolution space into simple maneuvers, represented each by a simple dynamic model such as constant velocity or constant turning etc. This allows the method to be optimized for highly dynamic vehicles. Unfortunately classical IMM shows some drawbacks concerning some real time multi sensors applications. In this work, we focus on outdoor vehicle localization with asynchronous sensors in order to report these drawbacks and then propose a new solution. Many tests carried out with simulated and real data confirm the interest for using such a solution in our applications

Experimental comparison of Kalman Filters for vehicle localization

Localizing a vehicle consists in estimating its state by merging data from proprioceptive sensors (inertial measurement unit, gyrometer, odometer, etc.) and exteroceptive sensors (GPS sensor). A well known solution in state estimation is provided by the Kalman filter. But, due to the presence of nonlinearities, the Kalman estimator is applicable only through some alternatives among which the extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the divided differences of 1st and 2nd order (DD1 and DD2). We have compared these filters using the same experimental data. The results obtained are aimed at ranking these approaches by their performances in terms of accuracy and consistency