Multi-scale parallel temperature error processing for dual-mass MEMS gyroscope

Abstract

A temperature error processing method for a dual-mass micro-electromechanical system (MEMS) gyroscope based on multi-scale parallel model is proposed. At first, a sample entropy based bounded ensemble empirical mode decomposition (SE-BEEMD) is proposed to decompose the original signal into different scales, include noise-only scale, mixed scale and drift scale; then forward linear prediction (FLP) is employed to eliminate the noise at mixed scale and extreme learning machine (ELM) based model is employed to compensate the drift at drift scale, the two steps are carried out paralleled; at last the final results can be obtained after reconstruction. Experimental results show that: (1) compared to tradition serial model, the proposed parallel model can eliminate the temperature errors more effectively, and each parameter of Allan analysis is improved. Specially, the quantification noise reduced from 0.035μrad to 9.93e4μrad, angle random walk reduced from 2.13e-5/s1/2 to 7.94e-6/s1/2, bias instability reduced from 5.28e-4/s to 4.79e-4/s, rate random walk from 0.012/s3/2 to 0.092/s3/2 and angular rate ramp reduced from 0.013/s2 to 0.011/s2; (2) compared to traditional time consuming neural networks, the ELM has the best modeling accurate and shortest training time, which would be valuable for online temperature drift modeling and compensation.