Square root cubature Kalman filter-based aircraft attitude estimation method

Abstract

The invention relates to a square root cubature Kalman filter-based aircraft attitude estimation method which comprises the steps of setting an initial value and a Rodriguez parameter when calculating time updating, calculating a volume point value, calculating a quaternion error point, calculating a quaternion volume point, calculating an iteration quaternion and an iteration quaternion error, calculating an iteration volume point, estimating a one-step prediction state, estimating a square root factor prediction error covariance matrix, calculating a volume point in measurement updating, calculating an iteration volume point value, calculating prediction measurement estimation, calculating square root factor innovation covariance matrix estimation, calculating a cross covariance matrix, carrying out Kalman gain, estimating state updating, calculating an estimated error covariance matrix and a corresponding square root factor, updating the quaternion, and calculating a corresponding estimated error euler angle. According to the square root cubature Kalman filter-based aircraft attitude estimation method, the precision and the stability of aircraft attitude can be improved.

Description

technical field [0001] The invention belongs to the problem of aircraft attitude estimation of a nonlinear system, in particular to an aircraft attitude estimation method based on a square root volumetric Kalman filter. Background technique [0002] Nonlinear filtering is one of the hot topics in the field of signal processing, target tracking and control. In particular, the research on nonlinear filtering under the framework of Kalman filtering is still a very popular problem, which has received more and more attention in recent years. Follow and study. [0003] As we all know, the essence of attitude estimation is a typical nonlinear filtering problem. Traditional nonlinear filters cannot be directly used to deal with aircraft attitude determination in most cases. This is because available pose estimators require nonlinear filters that can well incorporate the actual pose estimation context. That is, pose estimation principles must be integrated into the nonlinear filte...

AI Technical Summary

Problems solved by technology

This strategy enables UKF to significantly improve the performance of EKF. Although UKF has been used in many applications, it still has many shortcomings, especially in high-dimensional situations.

At the same time, the covariance matrix cannot always be guaranteed to be positive definite during the UKF calculation process.

Therefore, the use of UKF is still limited

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