Small unmanned aerial vehicle MARG heading and attitude estimation method based on adaptive EKF algorithm

Abstract

The invention relates to a small unmanned aerial vehicle MARG heading and attitude estimation method based on an adaptive EKF algorithm, and belongs to the technical field of integrated navigation. According to the method, firstly, measurement updating of a magnetometer and an acceleration meter are carried out separately, magnetometer error evaluation is added, when the magnetometer error is large, the magnetometer error can be isolated, and interference on horizontal attitude estimation is avoided; secondly, the invention further provides an adaptive filtering algorithm for the external acceleration, in order to improve the estimation precision, a triaxial acceleration module value is not directly judged, a triaxial residual error is analyzed, and then corresponding measurement noise issubjected to self-adaptive adjustment, so that useful acceleration information is prevented from being lost, and the attitude estimation precision is improved.

Description

technical field [0001] The invention belongs to the technical field of integrated navigation, and in particular relates to a method for estimating the MARG attitude of a small unmanned aerial vehicle based on an adaptive EKF algorithm. Background technique [0002] Due to its size and cost constraints, small UAVs usually use low-cost MEMS inertial devices as the main sensor of their navigation system, combined with magnetometer and GPS satellite positioning system for integrated navigation. When the number of satellites is insufficient during flight or when flying indoors, it is necessary to switch to the heading and attitude mode (AHRS) to provide attitude information. Commonly used AHRS attitude algorithms include complementary filter method, gradient descent method, and Kalman filter, etc., all of which are based on the MARG (magnetic, angular rate, and gravity) sensor measurement model for attitude estimation. The first two algorithms are relatively easy to implement an...

AI Technical Summary

Problems solved by technology

The first two algorithms are relatively easy to implement, and do not need special modeling for the system. In most cases, they can meet the flight requirements. However, in the case of large maneuvering flight or magnetic interference, the accuracy of the attitude angle will decrease and the error will be reduced. Slow convergence speed and other conditions will cause the aircraft to oscillate and spin, which will affect flight safety

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