In this paper, we propose an effective algorithm to estimate orientation angles (roll, pitch, and yaw) from Inertial Measurement Unit (IMU). This algorithm uses two adaptive extended Kalman filters (AEKF) to estimate the Direction Cosine Matrix (DCM), the external acceleration and the magnetic disturbance. First 6-state filter estimates three elements in the third row of the DCM and the external acceleration on three axes. The second one estimates three elements in the first row of the DCM and the magnetic disturbance on three axes. The last three elements of the DCM are computed from the DCM orthogonalization property. This method overcomes original problems when IMU is moved by external acceleration, and it is disturbed in magnetic environment. In addition, it helps reduce effort on computation from 15-states filter to two 6-states filters. The performance of proposed algorithm is verified by applying it to a 9-DOF IMU and testing its accuracy in various conditions. Experiment results have shown that the proposed algorithm achieves accurate estimation of orientation. The RMS error of all three angles is less than 2º.
This paper addresses the development of an integrated navigation system based on MEMS inertial sensors and GPS for model aircraft. The objective of this paper is to propose a loosely coupled GPS/INS integration approach using Extended Kalman filter to estimate aircraft's position, velocity, and attitude. Special attention is paid to the computational performance of the algorithm and the handling of GPS outages. The simulation results based on a typical high dynamic flight trajectory validate that the proposed filter is applicable to flight navigation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.