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MEMS (Micro Electronic Mechanical System) gyro data processing method based on wavelet threshold de-noising and FAR (Finite Automaton Recognizable) model

A technology of wavelet threshold denoising and data processing, applied in gyro effect for speed measurement, gyroscope/steering sensing equipment, complex mathematical operations, etc., can solve the problem of large output noise of MEMES gyro, achieve enhanced positioning performance, and solve output problems Large noise, overcoming the effect of large random drift modeling error

Inactive Publication Date: 2012-01-11
BEIHANG UNIV
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Problems solved by technology

[0009] The object of the invention is to: overcome the deficiencies in the prior art, provide a kind of MEMS gyro data processing method based on wavelet threshold value denoising and FAR model, solve the problem that MEMES gyro output noise is big, improve signal-to-noise ratio, and can be used for gyro Accurate modeling of random drift to improve MEMS gyroscope output accuracy

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  • MEMS (Micro Electronic Mechanical System) gyro data processing method based on wavelet threshold de-noising and FAR (Finite Automaton Recognizable) model
  • MEMS (Micro Electronic Mechanical System) gyro data processing method based on wavelet threshold de-noising and FAR (Finite Automaton Recognizable) model
  • MEMS (Micro Electronic Mechanical System) gyro data processing method based on wavelet threshold de-noising and FAR (Finite Automaton Recognizable) model

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Embodiment Construction

[0029] The combined data processing method in the present invention is proposed aiming at the problems of large noise and serious random drift in the MEMS gyroscope output signal. Among them, wavelet threshold denoising can be used for noise removal of gyro static and dynamic signals; while the random drift modeling of the FAR model needs to be based on the static output of the gyro, that is, modeling under static conditions, but the model can be used under dynamic conditions after establishment.

[0030] The flow chart of this combined data processing method is as follows figure 1 As shown, its specific implementation steps are as follows:

[0031] 1. First collect the original output signal of the MEMS gyroscope for a period of time, and it needs to be a static signal for a period of time.

[0032] 2. Perform wavelet threshold denoising on the original output signal of MEMS gyroscope, figure 2 The flow chart of wavelet threshold denoising, the specific implementation proc...

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Abstract

The invention discloses an MEMS (Micro Electronic Mechanical System) gyro data processing method based on wavelet threshold de-noising and an FAR (Finite Automaton Recognizable) model. For solving the problem that large amount of noise exists in the MEMS gyro output signal, the wavelet threshold de-noising method is used to process the gyro output signal to filter the noise and improve the signal-noise ratio; aiming at a static signal formed by de-noising gyro wavelet, a polynomial fitting method is used for the compensation to the gyro deterministic drift, wherein a residual error after the compensation is a random drift of the gyro, that is to say, a sample sequence required by modeling the random drift of the gyro is obtained; and aiming at modeling the random drift of the MEMS gyro athigher precision, the FAR model is used for modeling the random drift. The invention solves the problem of high output noise of the MEMS gyro, improves the signal-noise ratio and can accurately modelthe random drift of the gyro, thereby improving the output precision of the MEMS gyro.

Description

technical field [0001] The invention relates to the field of MEMS (Micro Electro Mechanical System, Micro Electro Mechanical System) gyroscope output data processing, in particular to a MEMS gyroscope combined data processing method based on wavelet threshold denoising and FAR (Functional coefficient Autoregressive. Function coefficient autoregressive) model. Background technique [0002] With the development of MEMS technology, MEMS gyroscopes have been widely used in low-cost inertial navigation systems due to their small size, light weight, low cost, and low power consumption. However, limited by the manufacturing process and technical level, the current accuracy of MEMS gyroscopes is lower than that of traditional gyroscopes, and its errors have become the main cause of errors in inertial navigation systems. MEMS gyroscope errors can generally be divided into scale factor error, deterministic drift, random drift and random noise. Scale factor error and deterministic dri...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C19/00G06F17/14
Inventor 丛丽秦红磊邢菊红
Owner BEIHANG UNIV
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