BP neural network-based MEMS gyro random error compensation method

Abstract

The invention discloses a BP neural network-based MEMS gyro random error compensation method. The method comprises the following steps: collecting original data of an MEMS gyro; preprocessing the original data through wavelet filtering; setting the input quantity and the output quantity of a BP neural network; training the data to establish a BP neural network-based MEMS gyro random error model; and compensating an MEMS gyro random error through the BP neural network-based MEMS gyro random error model. The input quantity of the BP neural network is constructed through data difference, the algorithm is simple, and the precision is high, so the method can effectively improve the accuracy and the reliability of MEMS gyro random error compensation, is simple and is easy to realize.

Description

technical field [0001] The invention relates to the technical field of inertia, in particular to a MEMS gyroscope random error compensation method based on a BP neural network. Background technique [0002] MEMS (Micro Electro Mechanical System, Micro Electro Mechanical System) gyroscope is a new type of all-solid-state gyroscope based on micro-mechanical electronic systems. Compared with laser gyroscopes, fiber optic gyroscopes or traditional mechanical gyroscopes, it has small size, low cost, and weight Lightweight, impact-resistant, and reliable, it is widely used in pedestrian navigation, small unmanned aerial vehicle, underwater robot, construction machinery and other fields. However, affected by the current manufacturing process and environment of MEMS inertial devices, MEMS gyroscopes still have shortcomings such as large random noise and low precision. On the one hand, a higher-precision MEMS gyroscope can be designed by improving the process level; The influence of...

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Problems solved by technology

[0005] The method of modeling the random error of MEMS gyroscope in the related technology mostly adopts the combination of two or more algorithms, such as the combination of genetic algorithm and neural network algorithm, the combination of time series analysis and particle filter algorithm, etc. Modeling, even if better results can be achieved, but this increases the complexity of calculations, making the amount of calculations relatively large, and the real-time performance of MEMS gyroscope random error modeling and compensation is also affected

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