Adaptive intensity noise suppression of fiber optic gyroscopes based on period LMS algorithm

Abstract

Reducing the noise level of fiber optic gyroscope (FOG) has been paid much attention. The relative intensity noise (RIN) of the light source is a dominant noise source and different methods have been used to reduce the RIN. However, the performance of the RIN suppression methods suffering from parameter variation is rarely discussed. In this paper, we introduced a method that uses the period LMS (PLMS) adaptive filter in the FOG system with noise subtraction implementation to ensure the sensor’s performance in varying environment. By analyzing the sampled detector signals in the system, the parameters in the system are determined based on the convergence requirement and noise performance of PLMS. Simulations as well as experiments of a FOG system with the PLMS algorithm implemented in the signal processing board are used to verify the feasibility of the method. The performance comparison between the PLMS filter and the predetermined Wiener filter in different temperature is tested and analyzed, which reveals the variability of the system parameters and verifies the necessity and ability of the introduced method to adapt to the varying environment.