Self-calibration level fusion method based on distribution diagrams and grouping estimation algorithm
Yuanze Liu, Jiawei Zhang, Mingbao Li · 2010
Due to the original data from homogenous sensor interfered by all kinds of noise signals in the actual industry process, it is essentially to eliminate the false senor or information. Sensor fusion method allows extracting information from several different sources to integrate them into single signal or information. The architecture of multi-sensor data fusion for detecting system in the industry process is presented in this paper firstly. According to the functional characteristic of self-calibration layer for the operating homogenous sensors, the distribution diagrams and grouping estimation method is adopted without any prior information from each sensor. Numerical studies show that using distribution diagrams and grouping estimation can eliminate successfully the missing errors of multiple information acquisition. The distribution diagrams and grouping estimation method and arithmetic averaging method are investigated respectively. Comparison the simulation results, the former can supply reliable data even if single sensor or several sensors are failed, with more precise and accuracy measured value than the arithmetic averaging method. Data fusion method in the self-calibration layer can eliminate uncertainty factors effectively. Therefore, it can improve the system performance in adaptability and robust.