Multi-Temperature and Humidity Data Fusion Algorithm Based on Kalman Filter

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.