TY - GEN
T1 - Bayesian Estimation of A Periodically-Releasing Biochemical Source Using Sensor Networks
AU - Hu, Liang
AU - Su, Jinya
AU - Hutchinson, Michael
AU - Liu, Cunjia
AU - Chen, Wen Hua
N1 - Funding Information:
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.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - 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.
AB - 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.
KW - Atmospheric dispersion model
KW - Bayesian estimation
KW - Sensor networks
KW - Source-term estimation
UR - http://www.scopus.com/inward/record.url?scp=85056897273&partnerID=8YFLogxK
U2 - 10.1109/CONTROL.2018.8516751
DO - 10.1109/CONTROL.2018.8516751
M3 - Published conference contribution
AN - SCOPUS:85056897273
T3 - 2018 UKACC 12th International Conference on Control, CONTROL 2018
SP - 107
EP - 112
BT - 2018 UKACC 12th International Conference on Control, CONTROL 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - UKACC 12th International Conference on Control, CONTROL 2018
Y2 - 5 September 2018 through 7 September 2018
ER -