Numerical investigation of Gaussian filters with a combined type Bayesian filter for nonlinear state estimation
This study presents a numerical comparison of three filtering techniques for a nonlinear state estimation problem. We consider an Extended Kalman Filter (EKF), an Unscented Kalman Filter (UKF) and a combined type of Particle Filter, so-called Extended Particle Filter (EPF), for the state estimation...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2018
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| Online Access: | https://hdl.handle.net/10356/89841 http://hdl.handle.net/10220/47133 |
| _version_ | 1826121238651600896 |
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| author | Mehndiratta, Mohit Prach, Anna Kayacan, Erdal |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Mehndiratta, Mohit Prach, Anna Kayacan, Erdal |
| author_sort | Mehndiratta, Mohit |
| collection | NTU |
| description | This study presents a numerical comparison of three filtering techniques for a nonlinear state estimation problem. We consider an Extended Kalman Filter (EKF), an Unscented Kalman Filter (UKF) and a combined type of Particle Filter, so-called Extended Particle Filter (EPF), for the state estimation for a re-entry vehicle system. The challenge in state estimation for this system is presence of significant nonlinearities in the process and measurement models. The performance aspects for the comparison include computation time, simulation time step, and effect of the choice of the initial conditions for the state estimate and covariance. Also, an investigation of the effect of the number of particles for EPF is performed. Simulation results illustrate that although EPF is computationally more expensive than EKF and UKF, it is less affected by the choice of initial conditions and simulation time step size. |
| first_indexed | 2024-10-01T05:29:26Z |
| format | Journal Article |
| id | ntu-10356/89841 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:29:26Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/898412023-03-04T17:16:47Z Numerical investigation of Gaussian filters with a combined type Bayesian filter for nonlinear state estimation Mehndiratta, Mohit Prach, Anna Kayacan, Erdal School of Mechanical and Aerospace Engineering Gaussian Filter DRNTU::Engineering::Mechanical engineering Nonlinear Estimation This study presents a numerical comparison of three filtering techniques for a nonlinear state estimation problem. We consider an Extended Kalman Filter (EKF), an Unscented Kalman Filter (UKF) and a combined type of Particle Filter, so-called Extended Particle Filter (EPF), for the state estimation for a re-entry vehicle system. The challenge in state estimation for this system is presence of significant nonlinearities in the process and measurement models. The performance aspects for the comparison include computation time, simulation time step, and effect of the choice of the initial conditions for the state estimate and covariance. Also, an investigation of the effect of the number of particles for EPF is performed. Simulation results illustrate that although EPF is computationally more expensive than EKF and UKF, it is less affected by the choice of initial conditions and simulation time step size. Published version 2018-12-20T07:53:51Z 2019-12-06T17:34:44Z 2018-12-20T07:53:51Z 2019-12-06T17:34:44Z 2016 Journal Article Mehndiratta, M., Prach, A., & Kayacan, E. (2016). Numerical investigation of Gaussian filters with a combined type Bayesian filter for nonlinear state estimation. IFAC-PapersOnLine, 49(18), 446-453. doi:10.1016/j.ifacol.2016.10.206 2405-8963 https://hdl.handle.net/10356/89841 http://hdl.handle.net/10220/47133 10.1016/j.ifacol.2016.10.206 en IFAC-PapersOnLine © IFAC 2016. This work is posted here by permission of IFAC for your personal use. Not for distribution. The original version was published in ifac-papersonline.net, DOI: 10.1016/j.ifacol.2016.10.206. 8 p. application/pdf |
| spellingShingle | Gaussian Filter DRNTU::Engineering::Mechanical engineering Nonlinear Estimation Mehndiratta, Mohit Prach, Anna Kayacan, Erdal Numerical investigation of Gaussian filters with a combined type Bayesian filter for nonlinear state estimation |
| title | Numerical investigation of Gaussian filters with a combined type Bayesian filter for nonlinear state estimation |
| title_full | Numerical investigation of Gaussian filters with a combined type Bayesian filter for nonlinear state estimation |
| title_fullStr | Numerical investigation of Gaussian filters with a combined type Bayesian filter for nonlinear state estimation |
| title_full_unstemmed | Numerical investigation of Gaussian filters with a combined type Bayesian filter for nonlinear state estimation |
| title_short | Numerical investigation of Gaussian filters with a combined type Bayesian filter for nonlinear state estimation |
| title_sort | numerical investigation of gaussian filters with a combined type bayesian filter for nonlinear state estimation |
| topic | Gaussian Filter DRNTU::Engineering::Mechanical engineering Nonlinear Estimation |
| url | https://hdl.handle.net/10356/89841 http://hdl.handle.net/10220/47133 |
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