Method of error compensation for FBG current sensor based on multisensor data fusion

As a novel measuring device, the FBG current sensor has developed rapidly which has some unexampled merits. In order to improve its accuracy and reliability, a method of error compensation based multisensor data fusion is presented. Data fusion is the process of combining data from multiple sensors to estimate or predict entity states. Multisensor data fusion seeks to combine data to measure the variables that may not be possible from a single sensor, reducing signals uncertainty and improving the accuracy performance of the measuring. In this paper, multisensor data fusion is used in error compensation of the FBG current sensor. It is applied FBG current sensor and two temperature sensors to measure the process variables related with the sensor error, such as current, temperature, noise etc, then a multisensor data fusion system based on RBF neural network is used to analyse and compensate the measuring error. The simulation results illustrate that this method is feasible and more effective.