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

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.

RFID unreliable data filtering by integrating adaptive sliding window and Euclidean distance

“…In this way, the negative reading and positive reading caused by unreasonable window size of fixed-length sliding window size method can be improved. However, this method only makes an adaptive improvement for the size of the sliding window on the whole without improving the redundant read in filtering methods [16,17]. Therefore, there are some limitations in the application of this system.…”

Using Artificial Neural Networks to Solve the Problem Represented by BOD and DO Indicators

“…Conducting an analysis of the state vector x (z, t) dependent on the time and length of the river, Streeter-Phelps Equations (1) and (4) including mass balance are transformed into the first order hyperbolic partial differential equations. BOD-DO mathematical model for i-th river segment is described with the equation [22,33]:…”

“…For the same characteristic, the moment t 0i+1 , determines the starting point in i+1-st segment. A set of line segments of the characteristic covers the space-time field, to which further considerations regarding the issues of river state estimation [20,[32][33][34][35] will be related. Measurements continuous in time can be performed at specified control points along the river.…”

“…Shifting down a particular river, encountering the located control points, the measurements taken for a given characteristic become discrete in time. Continuous measurements are described with the equation [33,34]:…”

“….. The edge conditions disappear because the characteristic concerns the whole field of considerations for the river [33,34].…”