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

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 representati...

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Vydané v:Sensors (Basel, Switzerland) Ročník 19; číslo 5; s. 1168
Hlavní autori: Park, Sebin, Gil, Myeong-Seon, Im, Hyeonseung, Moon, Yang-Sae
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI 07.03.2019
MDPI AG
Predmet:
denoising autoencoder
filtering accuracy
kalman filtering
measurement noise
sensor data
wavelet transform
kalman filtering
sensor data
denoising autoencoder
measurement noise
filtering accuracy
wavelet transform
ISSN:1424-8220, 1424-8220
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Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s19051168