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

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.

Distributed Kalman Filtering With Dynamic Observations Consensus

“…The input noises in the system are . The field is observed by a network of sensors (agents), where the measurement of sensor , , is (2) with the noise . In vector notation,…”

“…Assumption 1 (Global Detectability): The field dynamics (1) and the distributed observations (2) are globally detectable, but not necessarily locally detectable, i.e., the pair is detectable but are not necessarily detectable. This is reasonable since the optimal centralized estimator, with access to all the local measurements at every time index , will fail to obtain an unbiased estimate of the system with bounded MSE if the model is not globally detectable.…”

“…In the process, we characterize the noises both in the dynamics and the observations. We start from the system, observation, and noise equations, (1)- (2). The detailed analysis of the and leads to the following proposition.…”

“…Examples of time-varying random fields include time and space temperature variations over a large geographical area, [1], [2], dynamic states of a power grid, [3]- [5], tracking a group of cooperating vehicles, [6], [7], or beliefs in social networks, [8]. The field is observed by a sparse network of sensors (agents) that are inexpensive, low power, communicate locally, and can compute.…”