Measurement Noise Recommendation for Efficient Kalman Filtering over a Large Amount of Sensor Data

作     者：Park, Sebin Gil, Myeong-Seon Im, Hyeonseung Moon, Yang-Sae 

作者机构：Kangwon Natl Univ Dept Comp Sci Chuncheon Si 24341 Gangwon Do South Korea 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2019年第19卷第5期

页      面：1168-1168页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：National Research Foundation of Korea (NRF) - Korea government (MSIP) [NRF-2016R1C1B1015095] Korea Electric Power Corporation [R18XA05] 

主　　题：kalman filtering measurement noise sensor data filtering accuracy wavelet transform denoising autoencoder 

摘      要：To effectively maintain and analyze a large amount of real-time sensor data, one often uses a filtering technique that reflects characteristics of original data well. This paper proposes a novel method for recommending the measurement noise for Kalman filtering, which is one of the most representative filtering techniques. Kalman filtering corrects inaccurate values of input sensor data, and its filtering performance varies depending on the input noise parameters. In particular, if the noise parameters determined based on the user s experience are incorrect, the accuracy of Kalman filtering may be reduced significantly. Based on this observation, this paper addresses how to determine the measurement noise variance, a major input parameter of Kalman filtering, by analyzing past sensor data and how to use the estimated noise to improve the filtering accuracy. More specifically, to estimate the measurement noise variance, two analytical methods are proposed: one a transform-based method using a wavelet transform and the other a learning-based method using a denoising autoencoder. Experimental results show that the proposed methods estimated the measurement noise variance accurately and were superior to the experience-based method in the filtering accuracy.