Comparison Study on Self-Calibration Level Fusion Algorithm with Bayes Estimation Theory for Wood Drying Kiln Industry Process

Since the complex operation environment in wood drying kiln, the precision of lumber moisture content (LMC) is affected greatly with the ambient parameters which have closed multi-couple and correlated relation with them. Based on above reasons background, a comparison study on self-calibration level fusion algorithm based Bayes estimation theory for wood drying-kiln industry process is presented in this paper. Starting with analyzing the existing problems of LMC measured by individual sensor, we then put forward the architecture of multi-sensor data fusion for LMC measuring system. The performance of self-calibration level is detailed discussed. The technique of the determination of confidence distance and optimal fusion set, the total probability maximum algorithm, the Bayes estimation algorithm, and arithmetic averaging method are investigated respectively. Comparison the simulation results, a self-calibration fusion algorithm based Bayes estimation theory is determined.