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Kinetical Inflation and Quintessence by F-Harmonic Map

Journal of Modern Physics 2012年第11期3卷 1727-1731页

作者： Antonin Kanfon dominique lambert lambert Faculté des Sciences et Techniques Université d'Abomey-Calavi Cotonou Bénin University of Namur Namur Belgium

We were interested, along this work, in the phenomena of the quintessence and the inflation due to the F-harmonic maps, in other words, in the functions of the scalar field such as the exponential and trigo-harmonic maps. We showed that some F-harmonic map such as the trigonometric functions instead of the scalar field in the lagrangian, allow, in the absence of term of potential, reproduce the inflation. However, there are other F-harmonic maps such as exponential maps which can’t produce the inflation;the pressure and the density of this exponential harmonic field being both of the same sign. On the other hand, these exponential harmonic fields redraw well the phenomenon of the quintessence when the variation of these fields remains weak. The problem of coincidence, however remains.

关键词： F-Harmonic Maps Kinetical Inflation Kinetical Quintessence

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OKPS: A Reactive/Cooperative Multi-Sensors Data Fusion Approach Designed for Robust Vehicle Localization

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Positioning 2016年第1期7卷 1-20页

作者： Adda Redouane Ahmed Bacha dominique Gruyer Alain lambert Department COSYS IFSTTAR LIVIC Versailles France IEF/UMR CNRS 8622 Université Paris-Sud Centre d’Orsay Orsay France

This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF). The OKPS filter innovates by fitting its particles with a capacity of self-diagnose by means of the EKF covariance uncertainty matrix. The particles can therefore evolve by exchanging information to assess the optimized position of the ego-vehicle. The OKPS fuses data coming from embedded sensors (low cost INS, GPS and Odometer) to perform a robust ego-vehicle positioning. The OKPS is compared to the EKF filter and to filters using particles (PF and SPF) on real data from our equipped vehicle.

关键词： Localization Mobile Robotic Extended Kalman Filter Particle Swarm Optimization Particle Filter Data Fusion Vehicle Positioning GPS

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A Robust Hybrid Multisource Data Fusion Approach for Vehicle Localization

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Positioning 2013年第4期4卷 271-281页

作者： Adda Redouane Ahmed Bacha dominique Gruyer Alain lambert The French Institute of Science and Technology for Transport Development and Networks (IFSTTAR) COSYS Department LIVIC Laboratory 77 rue des Chantiers Versailles France.

In this paper, an innovative collaborative data fusion approach to ego-vehicle localization is presented. This approach called Optimized Kalman Swarm (OKS) is a data fusion and filtering method, fusing data from a low cost GPS, an INS, an Odometer and a Steering wheel angle encoder. The OKS is developed addressing the challenge of managing reactivity and robustness during a real time ego-localization process. For ego-vehicle localization, especially for highly dynamic on-road maneuvers, a filter needs to be robust and reactive at the same time. In these situations, the balance between reactivity and robustness concepts is crucial. The OKS filter represents an intelligent cooperative-reactive localization algorithm inspired by dynamic Particle Swarm Optimization (PSO). It combines advantages coming from two filters: Particle Filter (PF) and Extended Kalman filter (EKF). The OKS is tested using real embedded sensors data collected in the Satory’s test tracks. The OKS is also compared with both the well-known EKF and the Particle Filters (PF). The results show the efficiency of the OKS for a high dynamic driving scenario with damaged and low quality GPS data.

关键词： Localization Mobile Robotic Kalman Filter EKF Particle Swarm Optimization PSO Particle Filter Data Fusion Vehicle Positioning Navigation GPS

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