Novel adaptive Kalman attitude calculation method for unmanned aerial vehicles

Abstract

The invention provides a novel adaptive Kalman attitude calculation method for unmanned aerial vehicles, comprising: selecting an NED (north east down) right-handed coordinate system to create a discrete time-space model for a unmanned aerial vehicle dynamic attitude calculation system; determining a Sigma point set according to the dimensionality of a state variable; calculating weight value of the Sigma point set; calculating one-step prediction of the Sigma point set; subjecting a prediction value of the Sigma point set to weighted summation to obtain one-step prediction and covariance matrix of a system state quantity; generating a new Sigma point set via UT conversion according to the one-step prediction value; substituting the new Sigma point set to an observation equation in the discrete time-space model to obtain an observed quantity of prediction; subjecting the observed quantity of prediction to weighted summation to obtain a prediction average and covariance of the system; updating a Kalman gain matrix and the state and covariance of the system.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle control, in particular to a novel adaptive Kalman unmanned aerial vehicle attitude calculation method. Background technique [0002] Attitude is an important flight parameter of UAV, which is of great significance to the normal operation of UAV. At present, the commonly used attitude calculation methods include complementary filter attitude calculation algorithm, attitude calculation algorithm based on gradient descent method, attitude calculation algorithm based on Kalman filter, and particle filter attitude calculation algorithm. Among them, the complementary filter attitude algorithm mainly focuses on the adaptive adjustment of the filter coefficients to improve the algorithm's compensation for noise, and the accuracy is not high. Kalman filtering is currently a more suitable algorithm for multi-sensor attitude fusion, but the conventional Kalman filtering algorithm requires a s...

AI Technical Summary

Problems solved by technology

Among them, the complementary filter attitude algorithm mainly focuses on the adaptive adjustment of the filter coefficients to improve the algorithm's compensation for noise, and the accuracy is not high. Kalman filtering is currently a more suitable algorithm for multi-sensor attitude fusion, but the conventional Kalman filtering algorithm requires a system The statistical characteristics of noise and measurement noise are known, which will reduce the estimation accuracy of the convention

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