A Method of Improving the Accuracy of Inertial-Geomagnetism Combination Dynamic Attitude Determination

Abstract

The present invention provides a method for improving the dynamic attitude determination accuracy of inertia-geomagnetism combination. Using artificial neural network, aiming at complex three-dimensional movements such as human limb movement, firstly extract appropriate features from the discrete sampling time series output by the three-dimensional accelerometer, Then use the artificial neuron network to accurately estimate the magnitude of the linear acceleration of the carrier according to the above characteristics, and finally adjust the observation and process covariance of the extended Kalman algorithm in real time according to the above estimation results, thereby suppressing the influence of the linear acceleration of the carrier on the above algorithm, thereby improving the inertia ‑The attitude calculation accuracy of geomagnetic combination.

Description

technical field [0001] The invention relates to the technical field of sensors, in particular to a method for improving the inertia-geomagnetism combination dynamic attitude determination accuracy by using an artificial neuron network. Background technique [0002] The inertial-geomagnetic combination is a combination of sensors. Its hardware is mainly composed of a three-axis accelerometer, a three-axis gyroscope, a three-axis geomagnetic sensor, and some necessary signal processing units. Among them, the signal processing unit includes a preamplifier, a band-pass filter Devices, analog-to-digital converters, central processing units, power supply circuits, communication circuits, etc., are mainly used to realize the subsequent processing and transmission of sensor signals. The combined software program is run by the central processing unit, mainly including necessary processor initialization program, sensor data acquisition program, sensor error correction program, attitud...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: in order to solve the problem of the inertia-geomagnetism combined attitude calculation precision drop caused by carrier linear acceleration interference, the present invention provides a method for improving the inertia-geomagnetic combined dynamic attitude determination accuracy, the method first Appropriate features are constructed from the output of the accelerometer, followed by a network of artificial neurons to establish a nonlinear functional relationship between the above features and the magnitude of the linear acceleration of the carrier, and finally an artificial neural network (representing the magnitude of the linear acceleration of the carrier) The output of the meta-network adjusts the process of the extended Kalman algorithm and the observation noise covariance, thereby suppressing the influence caused by the linear acceleration of the carrier and improving the dynamic attitude calculation accuracy of the extended Kalman algorithm

[0006] At present, some targeted methods have been proposed to try to solve the problem of carrier linear acceleration interference

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