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Sensors 2017, 17(7), 1668; doi:10.3390/s17071668

Online Denoising Based on the Second-Order Adaptive Statistics Model

1
School of Computer Information and Engineering, Beijing Technology and Business University, Beijing 100048, China
2
Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
3
The Key Laboratory of Urban Security and Disaster Engineering, Ministry of Education, Beijing University of Technology, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Received: 24 April 2017 / Revised: 7 July 2017 / Accepted: 10 July 2017 / Published: 20 July 2017
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Abstract

Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter. The proposed method consists of two parts within a closed loop: the first one is to estimate the system state based on the second-order adaptive statistics model and the other is to update the adaptive parameter in the model using the Yule–Walker algorithm. Specifically, the state estimation process was implemented via the Kalman filter in a recursive way, and the online purpose was therefore attained. Experimental data in a reinforced concrete structure test was used to verify the effectiveness of the proposed method. Results show the proposed method not only dealt with the signals with colored noise, but also achieved a tradeoff between efficiency and accuracy. View Full-Text
Keywords: online denoising; the second-order adaptive statistics model; Kalman filter; Yule–Walker algorithm; real-time data processing online denoising; the second-order adaptive statistics model; Kalman filter; Yule–Walker algorithm; real-time data processing
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Yi, S.-L.; Jin, X.-B.; Su, T.-L.; Tang, Z.-Y.; Wang, F.-F.; Xiang, N.; Kong, J.-L. Online Denoising Based on the Second-Order Adaptive Statistics Model. Sensors 2017, 17, 1668.

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