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Sensors 2011, 11(11), 10266-10282; doi:10.3390/s111110266

Improved Kalman Filter Method for Measurement Noise Reduction in Multi Sensor RFID Systems

Department of Electronic Engineering, Dongguk University, 26, Pil-dong 3-ga, Jung-gu, 100-715, Seoul, Korea
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Received: 12 October 2011 / Revised: 24 October 2011 / Accepted: 24 October 2011 / Published: 28 October 2011
(This article belongs to the Special Issue Selected Papers from FGIT 2010)
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Abstract

Recently, the range of available Radio Frequency Identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less Mean Squared Error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.
Keywords: smart RFID tags; Kalman filter; neural network; multi-sensing environment; measurement noise reduction smart RFID tags; Kalman filter; neural network; multi-sensing environment; measurement noise reduction
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Eom, K.H.; Lee, S.J.; Kyung, Y.S.; Lee, C.W.; Kim, M.C.; Jung, K.K. Improved Kalman Filter Method for Measurement Noise Reduction in Multi Sensor RFID Systems. Sensors 2011, 11, 10266-10282.

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