Open AccessA Hybrid Approach‐Based Sparse Gaussian Kernel Model for Vehicle State Determination during Outage‐Free and Complete‐Outage GPS Periods
Author(s) -
Havyarimana Vincent,
Xiao Zhu,
Wang Dong
Publication year - 2016
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.16.0115.0617
Subject(s) - global positioning system , kalman filter , particle filter , covariance , control theory (sociology) , gaussian , kernel (algebra) , algorithm , kernel density estimation , square root , extended kalman filter , gps/ins , computer science , mathematics , assisted gps , statistics , artificial intelligence , telecommunications , physics , quantum mechanics , geometry , control (management) , combinatorics , estimator
To improve the ability to determine a vehicle's movement information even in a challenging environment, a hybrid approach called non‐Gaussian square root‐unscented particle filtering (nGSR‐UPF) is presented. This approach combines a square root‐unscented Kalman filter (SR‐UKF) and a particle filter (PF) to determinate the vehicle state where measurement noises are taken as a finite Gaussian kernel mixture and are approximated using a sparse Gaussian kernel density estimation method. During an outage‐free GPS period, the updated mean and covariance, computed using SR‐UKF, are estimated based on a GPS observation update. During a complete GPS outage, nGSR‐UPF operates in prediction mode. Indeed, because the inertial sensors used suffer from a large drift in this case, SR‐UKF‐based importance density is then responsible for shifting the weighted particles toward the high‐likelihood regions to improve the accuracy of the vehicle state. The proposed method is compared with some existing estimation methods and the experiment results prove that nGSR‐UPF is the most accurate during both outage‐free and complete‐outage GPS periods.