Kalman filtering with scheduled measurements - part I : estimation framework
This paper proposes an estimation framework under scheduled measurements for linear discrete-time stochastic systems. Both controllable and uncontrollable schedulers are considered. Under a controllable scheduler, only the normalized measurement innovation greater than a threshold will be communicat...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/97876 http://hdl.handle.net/10220/12321 |
| _version_ | 1824455853765296128 |
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| author | You, Keyou Xie, Lihua |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering You, Keyou Xie, Lihua |
| author_sort | You, Keyou |
| collection | NTU |
| description | This paper proposes an estimation framework under scheduled measurements for linear discrete-time stochastic systems. Both controllable and uncontrollable schedulers are considered. Under a controllable scheduler, only the normalized measurement innovation greater than a threshold will be communicated to the estimator. While under an uncontrollable scheduler, the time duration between consecutive sensor communications is triggered by an independent and identically distributed process. For both types of scheduler, recursive estimators that achieve the minimum mean square estimation error are derived, respectively. Moreover, necessary and sufficient conditions for stability of the mean square estimation error are provided. |
| first_indexed | 2025-02-19T03:44:49Z |
| format | Conference Paper |
| id | ntu-10356/97876 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:44:49Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/978762020-03-07T13:24:48Z Kalman filtering with scheduled measurements - part I : estimation framework You, Keyou Xie, Lihua School of Electrical and Electronic Engineering World Congress on Intelligent Control and Automation (10th : 2012 : Beijing, China) DRNTU::Engineering::Electrical and electronic engineering This paper proposes an estimation framework under scheduled measurements for linear discrete-time stochastic systems. Both controllable and uncontrollable schedulers are considered. Under a controllable scheduler, only the normalized measurement innovation greater than a threshold will be communicated to the estimator. While under an uncontrollable scheduler, the time duration between consecutive sensor communications is triggered by an independent and identically distributed process. For both types of scheduler, recursive estimators that achieve the minimum mean square estimation error are derived, respectively. Moreover, necessary and sufficient conditions for stability of the mean square estimation error are provided. 2013-07-25T09:18:30Z 2019-12-06T19:47:35Z 2013-07-25T09:18:30Z 2019-12-06T19:47:35Z 2012 2012 Conference Paper You, K., & Xie, L. (2012). Kalman filtering with scheduled measurements - Part I: Estimation framework. 2012 10th World Congress on Intelligent Control and Automation (WCICA). https://hdl.handle.net/10356/97876 http://hdl.handle.net/10220/12321 10.1109/WCICA.2012.6358249 en © 2012 IEEE. |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering You, Keyou Xie, Lihua Kalman filtering with scheduled measurements - part I : estimation framework |
| title | Kalman filtering with scheduled measurements - part I : estimation framework |
| title_full | Kalman filtering with scheduled measurements - part I : estimation framework |
| title_fullStr | Kalman filtering with scheduled measurements - part I : estimation framework |
| title_full_unstemmed | Kalman filtering with scheduled measurements - part I : estimation framework |
| title_short | Kalman filtering with scheduled measurements - part I : estimation framework |
| title_sort | kalman filtering with scheduled measurements part i estimation framework |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/97876 http://hdl.handle.net/10220/12321 |
| work_keys_str_mv | AT youkeyou kalmanfilteringwithscheduledmeasurementspartiestimationframework AT xielihua kalmanfilteringwithscheduledmeasurementspartiestimationframework |