An Intelligent Quaternion SVDCKF AHRS Estimation with Variable Adaptive Methods in Complex Conditions

TL;DR

This paper introduces an advanced quaternion SVDCKF with variable adaptive methods for small-UAV AHRS, improving accuracy and robustness in complex, dynamic conditions through innovative filtering and measurement noise tuning.

Contribution

It presents a novel SVDCKF algorithm combined with variable adaptive methods to enhance attitude estimation in complex UAV environments.

Findings

Superior attitude accuracy compared to CF and ESKF

Enhanced robustness in complex flying conditions

Effective measurement noise adaptation

Abstract

Aimed at solving the problem of Attitude and Heading Reference System(AHRS) in the complex and dynamic conditions for small-UAV, An intelligent Singular Value Decomposition Cubature Kalman Filter(SVDCKF) combined with the Variable Adaptive Methods(VAM) is proposed in this paper. Considering the nonlinearity of quaternion AHRS model and non-positive definite of the state covariance matrix, the SVDCKF algorithm is presented with both the SVD and CKF in order to better obtain the filter accuracy and reliability. Additionally, there are the different changes of the values in the accelerometer measurement resulting from the complex flying conditions. Thus, the VAM is designed to deal with three-axis values of the acceleration and tune intelligently the measurement noise matrix Ra. Moreover, the heading measurement from the three-axis values of the magnetometer is calculated according to the whether to use the three-axis values of the acceleration in the special situations. The simulation and experiment results demonstrate that the proposed filter algorithm has the more excellent attitude solution accuracy and robustness than both the Complementary Filter(CF) and the Error State Kalman Filter(ESKF).