Random signal de-noising based on empirical mode decomposition for laser gyro

The various random noises in laser gyro are the main factors generating errors. In accordance with the special property of laser gyro's noise, traditional filtering methods have many shortages to remove the noise. In order to restrain the random floating and improve the applicability precision for laser gyro, an improved de-noising algorithm based on the empirical mode decomposition(EMD) was proposed. The signal between the two adjacent zero crossing points within the intrinsic mode function(IMF) of EMD was defined as the modular cell by the method, which was treated as the basic analyzable object. Categories for the modular cell were judged by dealing with the amplitude of the cell, and then the filtering model was established. Evolutional rules of the amplitude for the modular cell were analyzed when the noisy signal corrupted by fractional Gaussian noise with different Hurst exponent were decomposed by EMD method, and threshold choosing rules used in Gaussian de-noising were also established. A signal de-noising test for laser gyro was performed to demonstrate the performance of the method. Compared with different de-nosing algorithms based on Allan variance method, the experimental results demonstrate the validity and superiority of the proposed algorithm.