The aim of this paper is to derail a fusion niethodfor land vehicle navigation using (parrial) GPS measurement and odomerric information. We focus here on a global esriniariori merhod which uses rhe available GPS signal with partial GPS ourage. The method deals with odometer and pseudo-range measures tliar contain the location parainefers of the vehicle. This estimation problem is solved by a Particle Filter, which has proved ifs benefits compared ro an Extended Kaluian Filter; since it deals wirh non-linear models arid sraristics.The Global Positioning System (GPS) is a key sensor for many navigation systems, and more particularly in the land vehicle navigation applications. In urban environment, a central problem lies in the periods of shading of the GPS receiver's antenna [6]. In order to provide a continuous navigation, the GPS is usually hybrided with additional sensors [9, 13, 11, 141. One of the most popular is the odometer, which is a wheel speed sensor available as standard component in Antilock Braking Systems (ABS). Odometers are electronic devices that generate digital pulses for each revolution of the wheel and allows an estimation of the distance covered by the vehicle.Many works on the subject use the Extended Kalman Filter (EKF) to achieve the GPSlodometer hybridation [I, 121.The EKF performs well in many practical situations, hut the unavoidable linearization stage of the state equations may lead to convergence or instability problems. Recent filtering methods, often merged as Sequential Monte-Carlo Methods [41, avoid these drawbacks due to their ability to deal with non-linear models and statistics. Some works are developed in this case for land vehicle navigation, and more specifically for GPSLlNS hybridation [?I, GPS/ABS hybridation and map matching 151, for instance.In these works, the odometer sensors are only used whenever the GPS measures are not available. The aim of our work is to propose a global modelling of the GPS/odometer fusion problem, with partial or total GPS outages (i.e. with less than four available satellites). The basic idea is to use the pseudo-range measures even if they are not enough to obtain a position estimation. To this end, one develops a particle-based solution that allows a direct integration of the non-linear odometric and pseudo-range measurements. The proposed method is compared with an usual Em-based navigation method with GPS satellites shading.
3-D navigation modelproblem.One deals here with a state modelling of this estimation be the state vector composed by the characteristics parameters of the vehicle. (y", vu3 U ) are respectively, the acceleration, the velocity and the position components along U
The main purpose of this paper is to present a fusion approach to bridge the period of Global Positioning System (GPS) outages using two proprioceptive sensors that are the Inertial Navigation System (INS) and the odometer in order to assure a continuous localization for land vehicle in urban areas where GPS signal blockage is very often. Odometer and GPS measures are exploited to correct inertial sensor errors. In fact, during GPS availability, INS is integrated with GPS to provide accurate localization solution; whereas during GPS outages, the odometer measurements are used to correct the INS error thereby improving the positioning accuracy and assuring the continuity of navigation solution. The problem of estimation of vehicle localization is realized by Kalman Filter (KF) that merges sensor measurements. The paper thus introduces results from simulation and real data.
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