MEMS (micro-electromechanical system) gyroscope random error modeling filtering method based on modified EMD (empirical mode decomposition)

Abstract

The invention discloses an MEMS (micro-electromechanical system) gyroscope random error modeling filtering method based on modified EMD (empirical mode decomposition), which comprises extracting an IMF (intrinsic mode function) component from an original signal through the EMD algorithm; modeling based on the extracted IMF component; subjecting a model formed by modeling to Kalman filtering, and compensating MEMS gyroscope random errors in real time. Measurement precision of a MEMS gyroscope can be improved.

Description

technical field [0001] The invention relates to the field of micro-electromechanical systems, in particular to an improved EMD-based MEMS gyroscope random error modeling and filtering method. Background technique [0002] With the rapid development of modern microelectronics and micromachining technology, MEMS gyroscopes based on MicroElectromechanical System (MEMS) technology are developing rapidly. Compared with other types of gyroscopes, MEMS gyroscopes are miniaturized and inexpensive. Low power consumption, easy installation and other advantages. MEMS has great development space and development value. At present, it has been more and more widely used in many fields. However, due to the characteristics of the components and the influence of the external environment, the measurement accuracy of the MEMS gyroscope is relatively low, and the random drift error is the main error source affecting the accuracy of the gyroscope. Therefore, in order to improve the stability a...

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Problems solved by technology

[0003] In recent years, scholars at home and abroad have conducted a lot of in-depth research on the random drift of MEMS gyroscopes, but these methods have their own advantages and disadvantages. Although the wavelet analysis method has a high resolution in the time-frequency domain, the process is complicated and the wavelet basis function The selection and fixed decomposition scale lead to its lack of good adaptability; the neural network modeling method theoretically has the ability to approximate nonlinear functions with arbitrary precision and has high-speed parallel computing capabilities, but it has a complex network structure and it is prone to over-fitting problems; and the method of time series analysis and the establishment of a reasonable random drift error ARMA model are the most widely used, and the accuracy of the model has achieved good results in gyroscope denoising. However, the premise of this method is that the sequence to be processed is a stationary sequence

In addition, Empirical Mode Decomposition (EMD) is a new adaptive signal processing method for non-stationary signals. This method does not require any prior knowledge of the signal and decomposes the original data into multiple The sum of the Intrinsic mode function (IMF) and the margin is a very effective method for data stabilization and denoising, but the disadvantage is that the theoretical support is insufficient. So far there is no complete EMD-based algorithm. mathematical model of

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