Research Article | OPEN ACCESS
Multi-Temperature and Humidity Data Fusion Algorithm Based on Kalman Filter
Yourong Chen, Jianfen Xu, Kejing Luo and Shuli Xu
Department of Information Science and Technology, Zhejiang Shuren University,
Hangzhou, 310015, China
Research Journal of Applied Sciences, Engineering and Technology 2013 6:2127-2132
Received: July 27, 2012 | Accepted: September 03, 2012 | Published: February 21, 2013
Abstract
In order to save system energy, enhance data-gathering accuracy and improve data-gathering efficiency in the temperature and humidity monitoring system based on wireless sensor networks, Multi-temperature and Humidity Data Fusion Algorithm based on Kalman Filter (MHDFA-KF) is proposed. In temperature and humidity sensor nodes, measured data are gathered and sent to sink node. In sink nodes, weighted fusion algorithm is used to fuse the received data and the fused data are sent to base station. In base station, Kalman filtering algorithm is used to filter the received data from sink nodes or sensor nodes. The time update equations and measurement update equations are used to iteratively calculate state variables and error covariance. Finally, the true value of temperature and humidity is obtained. The experimental results show that MHDFA-KF algorithm filters the data Gaussian noise, reduces the data measured error and obtain the true value. Under certain conditions, MHDFA-KF algorithm can be applied in temperature and humidity monitoring system based on wireless sensor networks. It has certain value.
Keywords:
Data fusion, Kalman filter, temperature and humidity data, wireless sensor networks,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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