Identification Method for RLG Random Errors Based on Allan Variance and Equivalent Theorem

Abstract An identification method using Allan variance and equivalent theorem is proposed to identify non-stationary sensor errors mixed out of different simple noises. This method firstly derives the discrete Allan variances of all component noises inherent in noise sources in terms of their different equations; then the variances are used to estimate the parameters of all component noise models; finally, the original errors are represented by the sum of the non-stationary component noise model and the equivalent model mixed out of the stationary and critically stationary component noises. Results of two examples for identification confirm the superiority of this approach regardless of the errors being stationary or not. The comparison of results of real ring laser gyro (RLG) errors processed by various methods shows that the proposed approach is more suited to depict the original noises than common ones.