A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment
In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/5/692 |
| _version_ | 1811304459205083136 |
|---|---|
| author | Zhu Xiao Vincent Havyarimana Tong Li Dong Wang |
| author_facet | Zhu Xiao Vincent Havyarimana Tong Li Dong Wang |
| author_sort | Zhu Xiao |
| collection | DOAJ |
| description | In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student’s t-distribution is adopted in order to compute the probability distribution function (PDF) related to the process and measurement noises, which are assumed to be non-Gaussian distributed. A computation approach based on Ensemble Kalman Filter (EnKF) is designed to cope with the mean and the covariance matrix of the proposal non-Gaussian distribution. A delayed Gibbs sampling algorithm, which incorporates smoothing of the sampled trajectories over a fixed-delay, is proposed to deal with the sample degeneracy of particles. The performance is investigated based on the real-world data, which is collected by low-cost on-board vehicle sensors. The comparison study based on the real-world experiments and the statistical analysis demonstrates that the proposed nGDPS has significant improvement on the vehicle state accuracy and outperforms the existing filtering and smoothing methods. |
| first_indexed | 2024-04-13T08:06:27Z |
| format | Article |
| id | doaj.art-004e1964deb44a2a80c6c0d11e3a1232 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:06:27Z |
| publishDate | 2016-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-004e1964deb44a2a80c6c0d11e3a12322022-12-22T02:55:07ZengMDPI AGSensors1424-82202016-05-0116569210.3390/s16050692s16050692A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian EnvironmentZhu Xiao0Vincent Havyarimana1Tong Li2Dong Wang3College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaIn this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student’s t-distribution is adopted in order to compute the probability distribution function (PDF) related to the process and measurement noises, which are assumed to be non-Gaussian distributed. A computation approach based on Ensemble Kalman Filter (EnKF) is designed to cope with the mean and the covariance matrix of the proposal non-Gaussian distribution. A delayed Gibbs sampling algorithm, which incorporates smoothing of the sampled trajectories over a fixed-delay, is proposed to deal with the sample degeneracy of particles. The performance is investigated based on the real-world data, which is collected by low-cost on-board vehicle sensors. The comparison study based on the real-world experiments and the statistical analysis demonstrates that the proposed nGDPS has significant improvement on the vehicle state accuracy and outperforms the existing filtering and smoothing methods.http://www.mdpi.com/1424-8220/16/5/692particle filterfixed-delay smoothingnon-Gaussian noiseEnsemble Kalman Filtervehicle localization |
| spellingShingle | Zhu Xiao Vincent Havyarimana Tong Li Dong Wang A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment Sensors particle filter fixed-delay smoothing non-Gaussian noise Ensemble Kalman Filter vehicle localization |
| title | A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment |
| title_full | A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment |
| title_fullStr | A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment |
| title_full_unstemmed | A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment |
| title_short | A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment |
| title_sort | nonlinear framework of delayed particle smoothing method for vehicle localization under non gaussian environment |
| topic | particle filter fixed-delay smoothing non-Gaussian noise Ensemble Kalman Filter vehicle localization |
| url | http://www.mdpi.com/1424-8220/16/5/692 |
| work_keys_str_mv | AT zhuxiao anonlinearframeworkofdelayedparticlesmoothingmethodforvehiclelocalizationundernongaussianenvironment AT vincenthavyarimana anonlinearframeworkofdelayedparticlesmoothingmethodforvehiclelocalizationundernongaussianenvironment AT tongli anonlinearframeworkofdelayedparticlesmoothingmethodforvehiclelocalizationundernongaussianenvironment AT dongwang anonlinearframeworkofdelayedparticlesmoothingmethodforvehiclelocalizationundernongaussianenvironment AT zhuxiao nonlinearframeworkofdelayedparticlesmoothingmethodforvehiclelocalizationundernongaussianenvironment AT vincenthavyarimana nonlinearframeworkofdelayedparticlesmoothingmethodforvehiclelocalizationundernongaussianenvironment AT tongli nonlinearframeworkofdelayedparticlesmoothingmethodforvehiclelocalizationundernongaussianenvironment AT dongwang nonlinearframeworkofdelayedparticlesmoothingmethodforvehiclelocalizationundernongaussianenvironment |