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Title: Bayesian estimation of a periodically-releasing biochemical source using sensor networks
Authors: Hu, Liang [Computer Science]
Su, Jinya
Hutchinson, Michael
Liu, Cunjia
Chen, Wen-Hua
Keywords: Source-term estimation
Bayesian estimation
Atmospheric dispersion model
Sensor networks
Issue Date: 2018
Publisher: © IEEE
Citation: HU, L. ... et al, 2018. Bayesian estimation of a periodically-releasing biochemical source using sensor networks. 2018 UKACC 12th International Conference on Control (CONTROL), Sheffield, UK, 5-7 September 2018, pp.107-112.
Abstract: This paper develops a Bayesian estimation method to estimate source parameters of a biochemical source using a network of sensors. Based on existing models of continuous and instantaneous releases, a model of discrete and periodic releases is proposed, which has extra parameters such as the time interval between two successive releases. Different from existing source term estimation methods, based on the sensor characteristic of chemical sensors, the zero readings of sensors are exploited in our algorithm where the zero readings may be caused by the concentration being below the threshold of the sensors. Two types of Bayesian inference algorithms for key parameters of the sources are developed and their particle filtering implementation is discussed. The efficiency of the proposed algorithms for periodic release is demonstrated and verified by simulation where the algorithm with the exploitation of the zero readings significantly outperforms that without.
Description: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/1 and the MOD University Defence Research Collaboration in Signal Processing.
Version: Accepted for publication
DOI: 10.1109/CONTROL.2018.8516751
URI: https://dspace.lboro.ac.uk/2134/33824
Publisher Link: https://doi.org/10.1109/CONTROL.2018.8516751
ISBN: 978-1-5386-2864-5/18
Appears in Collections:Conference Papers and Presentations (Aeronautical and Automotive Engineering)

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