MEMS gyroscope random error compensation method based on combination algorithm

Abstract

The invention discloses an MEMS gyroscope random error compensation method based on a combination algorithm. The MEMS gyroscope random error compensation method comprises the following steps: step 1,sampling angular rate data output by an MEMS gyroscope in continuous time as original output data; 2, selecting partial sampling data, and converting the partial sampling data into sample data; selecting a training sample and a verification sample, and setting extreme learning machine algorithm parameters; 3, taking the training sample as the input of an extreme learning machine random error model, and carrying out model learning; 4, performing improved verification on the extreme learning machine random error model obtained in the step 3 by using the verification sample; 5, judging whether the root mean square error RMSE of the model output value and the verification sample true value meets the requirement or not; and 6, establishing maximum posteriori Kalman filtering based on the randomerror model, and filtering gyro-drift data. The method has the advantages of error model nonlinearity and filtering noise self-adaptation, and can be suitable for random error compensation of an MEMSsingle-axis or multi-axis gyroscope dynamic and static system.

Description

technical field [0001] The invention belongs to the technical field of MEMS sensors, in particular to a MEMS gyroscope random error compensation method based on a combined algorithm. Background technique [0002] MEMS gyro is a sensor that can be sensitive to the angular velocity of the carrier. It has the advantages of small size, light weight, low cost, high reliability, and low power consumption. It is currently widely used in aviation, aerospace, navigation, weapons, automobiles, and environmental monitoring. field. As an important part of the inertial navigation field, the accuracy of the MEMS gyroscope directly affects the performance of the entire navigation system, so it is very important to improve the accuracy of the MEMS gyroscope. [0003] MEMS gyro measurement errors mainly come from processing errors, its own structure and external interference. According to the mechanism of MEMS gyroscope error generation, the analysis shows that the drift error of MEMS gyro...

AI Technical Summary

Problems solved by technology

In MEMS gyro random drift error correction, the most commonly used modeling and filtering methods are AR model and Kalman filter, that is, the error model is required to be linear, and the system noise and measurement noise are Gaussian white noise. Not only the error model has weak nonlinearity, but also the setting of Gaussian white noise in filtering is difficult to meet the requirements. Therefore, the traditional AR model-based and Kalman filtering methods have their own insurmountable nonlinearity and inaccurate noise estimation, resulting in random errors. Compensation for unsatisfactory defects

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