Mixture Particle Filter for Low Cost INS/Odometer/GPS Integration in Land Vehicles

Abstract

Global Positioning System (GPS) is currently the common solution for land vehicle positioning. However, GPS signals may suffer from blockage in urban canyons and tunnels, and the positioning information provided is interrupted. One solution to have continuous vehicle positioning is to integrate GPS with an inertial measurement unit (IMU) and the navigation solution is achieved using an estimation technique which is traditionally based on Kalman filter (KF). In order to have a low cost navigation solution for land vehicles, MEMS-based inertial sensors are used. To achieve a better performance during GPS outages, the speed derived from the vehicle odometer is used as a measurement update. To improve the positioning accuracy of the MEMS-based INS/Odometer/GPS integration, particle filtering (PF) is used as a nonlinear filtering technique, which does not need to linearize the models as in Extended KF (EKF). Because of PF ability to deal with nonlinear models, it can accommodate arbitrary sensor characteristics and motion dynamics. An enhanced version of PF is used which is called Mixture PF. While the Sampling/Importance Resampling (SIR) PF samples from the prior importance density and the Likelihood PF samples from the observation likelihood, the Mixture PF samples from both densities, then appropriate weighting is achieved followed by resampling. This mixture of importance densities leads to a better performance. The performance of this method is examined by road test trajectories in a land vehicle and compared to KF.