Indoor contaminant source estimation using a multiple model unscented Kalman filter
The contaminant source estimation problem is getting increasing importance due to more and more occurrences of sick building syndrome and attacks from covert chemical warfare agents. To monitor a building contamination condition, a number of sensors are connected through a network, and the sensor me...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2014
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| Online Access: | https://hdl.handle.net/10356/101970 http://hdl.handle.net/10220/19822 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6290526 |
| _version_ | 1824455670970187776 |
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| author | Yang, Rong Foo, Pek Hui Tan, Peng Yen See, Elaine Mei Eng Ng, Gee Wah Ng, Boon Poh |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yang, Rong Foo, Pek Hui Tan, Peng Yen See, Elaine Mei Eng Ng, Gee Wah Ng, Boon Poh |
| author_sort | Yang, Rong |
| collection | NTU |
| description | The contaminant source estimation problem is getting increasing importance due to more and more occurrences of sick building syndrome and attacks from covert chemical warfare agents. To monitor a building contamination condition, a number of sensors are connected through a network, and the sensor measurements are sent to a fusion center to estimate contaminant source information. An estimation algorithm is required such that timely actions can be taken to mitigate the adverse effects. This paper proposes a multiple model unscented Kalman filter (MM-UKF) to estimate the contaminant source location, the source emission rate and the release time. A simulation test is conducted on a computer generated three-story building. The results show that the MM-UKF algorithm can achieve real-time estimation. |
| first_indexed | 2025-02-19T03:41:54Z |
| format | Conference Paper |
| id | ntu-10356/101970 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:41:54Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/1019702019-12-06T20:47:39Z Indoor contaminant source estimation using a multiple model unscented Kalman filter Yang, Rong Foo, Pek Hui Tan, Peng Yen See, Elaine Mei Eng Ng, Gee Wah Ng, Boon Poh School of Electrical and Electronic Engineering International Conference on Information Fusion (15th : 2012) DRNTU::Engineering::Electrical and electronic engineering The contaminant source estimation problem is getting increasing importance due to more and more occurrences of sick building syndrome and attacks from covert chemical warfare agents. To monitor a building contamination condition, a number of sensors are connected through a network, and the sensor measurements are sent to a fusion center to estimate contaminant source information. An estimation algorithm is required such that timely actions can be taken to mitigate the adverse effects. This paper proposes a multiple model unscented Kalman filter (MM-UKF) to estimate the contaminant source location, the source emission rate and the release time. A simulation test is conducted on a computer generated three-story building. The results show that the MM-UKF algorithm can achieve real-time estimation. Published version 2014-06-19T03:15:40Z 2019-12-06T20:47:39Z 2014-06-19T03:15:40Z 2019-12-06T20:47:39Z 2012 2012 Conference Paper Yang, R., Foo, P. H., Tan, P. Y., See, E. M. E., Ng, G. W., & Ng, B. P. (2012). Indoor contaminant source estimation using a multiple model unscented Kalman filter. 2012 15th International Conference on Information Fusion (FUSION), 1854-1859. https://hdl.handle.net/10356/101970 http://hdl.handle.net/10220/19822 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6290526 en © 2012 International Society of Information Fusion. This paper was published in 2012 15th International Conference on Information Fusion (FUSION) and is made available as an electronic reprint (preprint) with permission of International Society of Information Fusion. The paper can be found at the following official URL:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6290526. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Yang, Rong Foo, Pek Hui Tan, Peng Yen See, Elaine Mei Eng Ng, Gee Wah Ng, Boon Poh Indoor contaminant source estimation using a multiple model unscented Kalman filter |
| title | Indoor contaminant source estimation using a multiple model unscented Kalman filter |
| title_full | Indoor contaminant source estimation using a multiple model unscented Kalman filter |
| title_fullStr | Indoor contaminant source estimation using a multiple model unscented Kalman filter |
| title_full_unstemmed | Indoor contaminant source estimation using a multiple model unscented Kalman filter |
| title_short | Indoor contaminant source estimation using a multiple model unscented Kalman filter |
| title_sort | indoor contaminant source estimation using a multiple model unscented kalman filter |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/101970 http://hdl.handle.net/10220/19822 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6290526 |
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