Title: Deep fusion method of IoT monitoring data based on extended Kalman filter

Authors: Jixiang Ding

Addresses: Department of Information Technology, Qingdao Vocational and Technical College of Hotel Management, Qingdao, Shandong Province, China

Abstract: To overcome the problems of low fusion accuracy and high data loss rate in traditional IoT monitoring data fusion methods, the paper proposes a deep fusion method for IoT monitoring data based on extended Kalman filtering. Firstly, obtain monitoring data from IoT sensors and correct the frequency domain characteristics of sensor signals. Secondly, wavelet coefficients are used to remove noise from the data. Then, calculate the Kalman gain of the monitoring data. Finally, the mean value of the node state is linearised and updated to achieve deep fusion of monitoring data through the fusion value and fusion variance of the node data. The results show that the data fusion accuracy of this method can reach 96%, and the minimum data missing rate is only 3%. The fusion effect is good and has certain application value.

Keywords: IoT monitoring data; data fusion; extended Kalman filtering; sensor signal; Kalman gain.

DOI: 10.1504/IJCAT.2024.141356

International Journal of Computer Applications in Technology, 2024 Vol.74 No.1/2, pp.19 - 25

Received: 30 Oct 2023
Accepted: 13 Feb 2024
Published online: 09 Sep 2024 *

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