Abstract
To solve the problem of micro‐electro‐mechanical system (MEMS) gyroscope noise, this paper presents a variational mode decomposition (VMD) method based on crow search algorithm. First, the signal was decomposed by variational mode decomposition for optimization of crow search algorithm (CSA‐VMD) method. The parameters required by the VMD method (penalty parameter α and decomposition number K) are given by the crow search algorithm, and then the signal is decomposed into the superposition of multiple subsignals, called intrinsic mode functions (IMFs). The sample entropy (SE) corresponding to each IMF is then obtained. By calculating the sample entropy, the noise signal can be divided into pure noise part, mixing part, and temperature drift part. Second, Savitzky–Golay smoothing denoising (SG) is used to filter the mixed noise signal to eliminate the influence of noise. Third, for the filtering of the drift part, the least square support vector machine optimized by the crow search algorithm (CSA‐LSSVM) was used to filter, so as to reduce the effect of temperature drift. Finally, the processed signal is reconstructed to achieve the goal of denoising. Through the results, it can be found that the optimized VMD and LSSVM using CSA algorithm can achieve more effective denoising. After using the method proposed in this paper, the angular random walk value is 1.1175 ∗ 10−4°/h/√Hz, and the bias stability is 0.0017°/h. Compared with the original signal, the two signals are optimized by 98.1% and 98.2%, respectively. It can be seen from the experimental results that the proposed CSA‐VMD method, SG method, and CSA‐LSSVM method can effectively eliminate noise effects.
Highlights
In recent years, the research of micro-electro-mechanical system (MEMS) gyroscope is endless, and it has been widely used in aviation, spaceflight, navigation, and civil electronic equipment. e reason for this is that micro-electro-mechanical system (MEMS) gyroscope has high efficiency, low price, low energy consumption, and other cost-effective characteristics [1]
In order to improve the shortcomings of ensemble empirical mode decomposition (EEMD), such as false mode, extra postprocessing, and large computation burden, and to avoid the problem of predecomposition and overdecomposition, this paper proposes a method to stratify the decomposed subsignals by using sample entropy
By testing the temperature characteristics of MEMS gyroscopes, we can verify the accuracy and feasibility of the proposed method—crow search algorithm (CSA)-variational mode decomposition (VMD)-sample entropy (SE)-smoothing denoising (SG). e gyroscopes and instruments used in the experiment are shown in Figure 8. e detection circuit is connected to the electrical signal through a metal pin and the printed circuit board is wrapped with a rubber pad to protect the printed circuit
Summary
The research of micro-electro-mechanical system (MEMS) gyroscope is endless, and it has been widely used in aviation, spaceflight, navigation, and civil electronic equipment. e reason for this is that micro-electro-mechanical system (MEMS) gyroscope has high efficiency, low price, low energy consumption, and other cost-effective characteristics [1]. E reason for this is that micro-electro-mechanical system (MEMS) gyroscope has high efficiency, low price, low energy consumption, and other cost-effective characteristics [1]. Ere are two kinds of temperature error processing methods: hardware compensation and software compensation [3]. By controlling the circuit and improving the structure of the gyroscope, the temperature performance of the gyroscope is improved and the hardware compensation is realized [4]. E appearance of software compensation makes up the disadvantage of high cost and complicated steps in the hardware compensation of MEMS gyroscope. Temperature compensation is the most commonly used software compensation method for various gyroscopes and MEMS gyroscopes when dealing with temperature error. In [7], improved empirical mode
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