Nine-axis attitude sensor integrated intelligent error compensation method based on neural network

An attitude sensor and neural network technology, applied in neural learning methods, biological neural network models, navigation through speed/acceleration measurement, etc., can solve the problems of error compensation accuracy, time-consuming, low-level, etc., and achieve short calibration time and calibration The effect of excellent precision

Pending Publication Date: 2022-07-05
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

Traditional error compensation experiments require the support of high-precision turntables, and detailed error compensation paths need to be formulated before calibration, which takes a long time
At the same time, there are problems that the error compensation results are difficult to migrate to different environments, and it is necessary to rely on other sensors or external reference turntables for error compensation before each application, and the accuracy is not high.

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  • Nine-axis attitude sensor integrated intelligent error compensation method based on neural network
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  • Nine-axis attitude sensor integrated intelligent error compensation method based on neural network

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Embodiment Construction

[0022] In order to make those skilled in the art better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application.

[0023] The present invention is a discrete and integrated intelligent error compensation method based on BP neural network and a discrete and integrated intelligent error method based on RBF neural network, which can improve the compensation accuracy and reduce the time spent on error compensation.

[0024] Wherein, the discrete intelligent error compensation method based on BP neural network includes the following steps:

[0025] S1: Collect the measurement output values ​​of nine-axis attitude sensors with different precisions, take the output value of the sensor with high precision as the true value, and the output value with low precision as the measurement val...

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Abstract

The invention provides a nine-axis attitude sensor integrated intelligent error compensation method based on a neural network, which can ensure the error compensation precision, reduce the time required for error compensation and improve the error compensation efficiency. A neural network is introduced to establish an intelligent error model, an input value of the neural network is a measured value of a nine-axis attitude sensor with a large error, and an output value of the neural network is a measured value of the nine-axis attitude sensor with a high precision, namely a small error. The nine-axis attitude sensor integrated error compensation method applying the neural network is suitable for a field calibration environment without a high-precision rotary table, the calibration precision is better than that of a traditional calibration method based on a least square method, and the calibration time is short.

Description

technical field [0001] The invention belongs to the technical field of attitude sensor error compensation, and in particular relates to a nine-axis attitude sensor integrated intelligent error compensation method based on a neural network. Background technique [0002] The nine-axis attitude sensor is composed of a three-axis gyroscope, a three-axis accelerometer and a three-axis magnetoresistive sensor, and is the core component for obtaining the carrier attitude. The nine-axis attitude sensor error compensation technology has become one of the key factors restricting the carrier to obtain high-precision attitude. The traditional nine-axis attitude sensor error compensation method: the gyroscope is sensitive to the angular rate of the carrier in the three-axis direction. The idea of ​​gyroscope error compensation is to give the gyroscope excitation signal, and to identify the error model through the collected gyroscope input-output values. The traditional error compensati...

Claims

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Application Information

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IPC IPC(8): G01C21/16G06N3/08
CPCG01C21/183G06N3/08
Inventor 汪进文董捷飔邓志红宋新宇
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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