GNSS and MIMU integrated navigation method, device and computer equipment

Abstract

The invention relates to a GNSS and MIMU combined navigation method, a system, computer equipment and a storage medium. The method comprises that speed data and position data of a satellite navigationsystem as well as speed data and position data of an inertial navigation system are obtained; according to an error value between the speed data of the satellite navigation system and the speed dataof the inertial navigation system as well as an error value between the position data of the satellite navigation system and the position data of the inertial navigation system, a Kalman filtering equation set is constructed; if a measuring value is greater than a first preset limit value, a mean square error predication equation in the Kalman filtering equation set is adjusted and a mean square error value is calculated according to the preset forgetting factor, and a gain coefficient is obtained; and a Kalman filtering result of the Kalman filtering equation set is obtained according to thegain coefficient, and thus, second speed data, second position data and attitude data of the inertial navigation system can be corrected. The method can be used to reduce influence of deviation of measurement data in an internal device in the inertial navigation system on a navigation result, and improve the accuracy of the navigation result resolved under the vibration direction of combined navigation.

Description

technical field [0001] The present application relates to the field of navigation technology, in particular to a GNSS and MIMU integrated navigation method, device, computer equipment and storage medium. Background technique [0002] With the continuous improvement of GNSS (Global Navigation Satellite System, global satellite navigation system) and the continuous development of miniature inertial measurement unit (Miniature Inertial Measurement Unit, MIMU), the integrated navigation system of GNSS and MIMU inertial navigation system gradually appears on the market, and Gradually applied to the field of drones. [0003] At present, the fusion algorithm used by GNSS and MIMU integrated navigation is generally the extended Kalman filter algorithm. However, after the extended Kalman filter has converged for a long time, the dependence of the Kalman filter on the state quantity of the past time is gradually increased, and the effect of new observations on the estimation and corr...

AI Technical Summary

Problems solved by technology

However, after the extended Kalman filter has converged for a long time, the dependence of the Kalman filter on the state quantity of the past time is gradually increased, and the effect of new observations on the estimated correction of the state quantity is gradually reduced.

If the integrated navigation of GNSS and MIMU is applied to UAVs, the combined vibration of motor vibration, blade vibration and airflow vibration in UAVs will lead to deviations in the measurement data of inertial devices in the inertial navigation system, and in UAVs The combination of motor vibration, blade vibration and airflow vibration is relatively irregular, and the original vibration state has little influence on the current vibration state. It is difficult to guarantee the accuracy of integrated navigation under vibration conditions by using the extended Kalman filter algorithm.

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