Random Interference Signal Decomposition and the Normalized Filtering Method of an Optical Fiber Current Transducer

Focusing on the problem of characteristic decomposition and filtering of random interference information measured by an optical fiber current transducer (OFCT), a signal filtering algorithm by combing complete ensemble empirical mode decomposition (CEEMD) with normalized autocorrelation function, is proposed. The CEEMD feature decomposition model of the OFCT signal is established and multiple eigenmode functions of the measured signal are extracted. The normalized autocorrelation function models of different types of intrinsic mode function (IMF) are established. By extracting the characteristics of the autocorrelation function, high-weight IMFs are selected. After the mean filtering process is performed on other IMFs, the signal reconstruction is performed together with the effective modal components. With the premise of signal statistical learning and structural risk minimization principles, a support vector regression model is established to classify the data by linear fitting. The more reliable current information after filtered is obtained. Experiment results demonstrate that the proposed signal filtering algorithm by combining the advantages of CEEMD and normalized autocorrelation function decomposes the signal according to the time-scale characteristics of OFCT data itself, without pre-setting any basis functions. The root mean square error of optimized data is reduced by 39.3%, and the signal quality is greatly improved.