Abstract
For the sake of decreasing the effects of noise and temperature error on the measurement accuracy of micro-electro-mechanical system (MEMS) gyroscopes, a denoising and temperature drift compensation parallel model method based on wavelet transform and forward linear prediction (WFLP) and support vector regression based on the cuckoo search algorithm (CS-SVR) is proposed in this paper. First, variational mode decomposition (VMD) is proposed in this paper, which is aimed at dividing the output signal of the gyroscope into intrinsic mode functions (IMFs); then, the IMFs are classified into three features—drift, mixed, and pure noise features—by the sample entropy (SE) value. Second, a wavelet transform and forward linear prediction (WFLP) are combined to remove the noise from the mixed features. Meanwhile, the drift feature is compensated by support vector regression based on the cuckoo search algorithm (CS-SVR). Finally, through reconstruction, the final signal is obtained. Experimental results demonstrate that the VMD-SE-WFLP-CS-SVR method proposed in this paper can decrease noise and compensate the temperature error effectively (angular random walking value is optimized from 1.667°/√h to 0.0667°/√h and the bias stability is reduced from 30°/h to 4°/h). In terms of denoising, the performance of the WFLP algorithm is superior to the wavelet threshold and FLP, as it combines their advantages; furthermore, in terms of temperature compensation, the proposed CS-SVR algorithm uses the cuckoo search algorithm to find the optimal parameters of SVR, improving the accuracy of the model.
Highlights
Micro-electro-mechanical system (MEMS) gyroscopes have been widely studied and employed in aviation, attitude controlling, and guidance, on account of their excellent precision, fast response, and low cost, among other advantages [1,2,3]
The denoising method and temperature error modeling are designed for micro-electro-mechanical system (MEMS) gyroscopes, a wavelet transform and forward linear prediction (WFLP) algorithm is introduced for eliminating the noise of output signal, and the cuckoo search (CS)-support vector regression (SVR) model is proposed for temperature error modeling
This paper introduced sample entropy (SE) to divide the intrinsic mode functions (IMFs) into different features and, to recombine the IMFs into a new subseries based on their approximate SE values, greatly reducing the computational complexity
Summary
Micro-electro-mechanical system (MEMS) gyroscopes have been widely studied and employed in aviation, attitude controlling, and guidance, on account of their excellent precision, fast response, and low cost, among other advantages [1,2,3]. We propose a variational mode decomposition [23] with sample entropy, which can effectively avoid modal aliasing and over-decomposition while accurately reflecting the characteristics of the signal Machine learning algorithms such as extreme learning machine (ELM) [24], random forest (RF) [25], and logistic regression [26] have become an important research issue and have been widely applied in a variety of fields [27]. The denoising method and temperature error modeling are designed for MEMS gyroscopes, a wavelet transform and forward linear prediction (WFLP) algorithm is introduced for eliminating the noise of output signal, and the CS-SVR model is proposed for temperature error modeling.
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