A spatial-temporal fusion algorithm based support degree and self-adaptive weighted theory for multi-sensor

Due to the differ sensors distributing position, operation performance and some uncertainty factors effect in the industrial process, the measured parameter´s excursion inevitably is caused in the real world. To obtain the accurate measuring value, a spatial-temporal fusion algorithm based support degree and self-adaptive weighted theory is put forward in this paper. Considering the temporal and special domain feature, the architecture of spatial-temporal fusion modeling is built. The temporal fusion method based support degree and recursive estimation is proposed to determine consistent and reliable estimation of measured variables with setting the support degree function. The data of the n moment from the one sensor are estimated by temporal fusion method. The spatial fusion based on the adaptive weighted method is determined by Lagrange multiplier method to solving the optimal weighted factors. The simulation results show that the spatial-temporal fusion algorithm is effective. Then, the algorithm is applied for the detecting lumber moisture content in the real world. It is verified by the accuracy and reliability for the measured parameter.