A Hybrid Approach‐Based Sparse Gaussian Kernel Model for Vehicle State Determination during Outage‐Free and Complete‐Outage GPS Periods. Issue 3 (1st June 2016)

• Record Type: Journal Article
• Authors: Havyarimana, Vincent; Xiao, Zhu; Wang, Dong
• Is Part Of: ETRI journal. Volume 38:Issue 3(2016)
• Page Start: 579
• Page End: 588
• Publication Date: 2016-06-01
• Subjects: Non‐Gaussian model -- sparse kernel density estimation -- SR‐UPF -- vehicle state errors -- GPS outages -- smartphone sensors; Telecommunication -- Periodicals; Electronics -- Periodicals; Electronics; Telecommunication; Periodicals; Periodicals; 621.38205
• DOI: 10.4218/etrij.16.0115.0617
• Languages: English
• ISSNs: 1225-6463

Abstract

Abstract : 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.