Determination of Chemical Point Source Using Distributed Algorithm in Sensors Network
Chemical source parameters determination using wireless sensor networks in an arbitrary environment is a multi-faceted problem. Potential applications include security, environmental and industrial monitoring, as well as pollution control. In this paper, we propose a distributed estimation method within the Bayesian filtering framework called an iterative in-network approach, which makes use of the extended Kalman filter (EKF) and unscented Kalman filter (UKF) independently to detect and localize a chemical source and determine its emission rate. The implementation of estimation method is based on a dispersion physical model and concentration signals measured by chemical sensors in a wireless sensor networks. Simulation results show that the UKF algorithm performs better in estimation accuracy and less communication cost than the EKF.
Chemical Point Source Parameter Determination Extended Kalman Filter Unscented Kalman Filter
Zhang Yong Zuo Hui Gao Dongqiang Wang Zhihua
College of Information, Tianjin University of Commerce, Tianjin, China Measurement and verification Office of Xiqing District, Tianjin, China Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
3385-3389
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）