Method for inhibiting random drift errors of vehicle-mounted MEMS gyroscope

Abstract

The invention discloses a method for inhibiting random drift errors of a vehicle-mounted MEMS gyroscope. According to the algorithm, modeling and filtering are carried out on the output data of the vehicle-mounted MEMS gyroscope sensor, so that the accuracy and stability of the output data of the vehicle-mounted MEMS gyroscope sensor are improved. The method aims at solving the problem that largeerrors exist in the output data of the vehicle-mounted MEMS gyroscope sensor. The method comprises the following steps: firstly, checking the stability of the output data of a selected MEMS gyroscopeby adopting a unit root checking method, and constructing a time sequence ARMA model through the change characteristics of an autocorrelation coefficient graph and a partial correlation coefficient graph of the output data of the MEMS gyroscope in combination with a minimum information criterion; then, applying the ARMA model to a discrete Kalman filtering equation to obtain filtered data; and finally, verifying the effectiveness of the algorithm developed by the invention through experiments. According to the method, the random error of the gyroscope is effectively suppressed, and the signal-to-noise ratio of the output signal is improved.

Description

technical field [0001] The invention relates to the technical field of application of MEMS gyroscope sensors, in particular to a method for suppressing random drift errors of vehicle-mounted MEMS gyroscopes. Background technique [0002] Micro Electro Mechanical Systems (MEMS) gyroscopes have the advantages of low cost, small size, light weight, low price, easy mass production, and integration, and are used in many different fields. Low-cost and low-precision MEMS gyroscopes are widely used in mobile phones, somatosensory game platforms and some wearable devices, which makes human-computer interaction reach a new level; mid-level MEMS gyroscope sensors are mainly used in industrial fields, such as electronic vehicle stability systems , GPS-assisted navigation, electronic stability control, medical equipment and other fields; in the field of military industry, high-precision MEMS gyroscopes tend to replace low-precision fiber optic gyroscopes, which can meet the requirements ...

AI Technical Summary

Problems solved by technology

Obviously, the aforementioned μ and δ have been affected by potential errors in the process, often leading to misjudgments in some cases

In addition, some scholars have used the first-order Gauss-Markov random error modeling method to model the error of the output data of the low-cost MEMS gyroscope, but the autocorrelation sequence of this method has the characteristics of inaccurate judgment, and the modeling Afterwards, certain adjustment calculations are required to obtain a more accurate model, so this method is not widely used in the random error modeling of low-cost MEMS gyroscopes.

[0005] Through the above analysis, it can be seen that there are deficiencies in the existing methods for dealing with random drift errors.

At the same time, there are relatively few research programs on the elimination of random drift errors of MEMS gyroscopes that can be put into use at present.

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