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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Hybrid Approach-Based Sparse Gaussian Kernel Model for Vehicle State Determination during Outage-Free and Complete-Outage GPS Periods

  • Havyarimana, Vincent (College of Computer Science and Electronic Engineering, Hunan University, State Key Laboratory of Integrated Service Networks, Xidian University) ;
  • Xiao, Zhu (College of Computer Science and Electronic Engineering, Hunan University, State Key Laboratory of Integrated Service Networks, Xidian University) ;
  • Wang, Dong (College of Computer Science and Electronic Engineering, Hunan University)
  • Received : 2015.07.06
  • Accepted : 2016.02.18
  • Published : 2016.06.01
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Abstract

To improve the ability to determine a vehicle's movement information even in a challenging environment, a hybrid approach called non-Gaussian square rootunscented 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.

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References

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