Abstract
A development procedure for a low-cost attitude and heading reference system (AHRS) based on the distributed filter has been proposed. The AHRS consists of three single-axis accelerometers, three single-axis gyroscopes, and one 3-axis digital compass. The initial attitude estimation is readily accomplished by using the complementary filtering. The attitude estimation for UAV flying in the real time is realized by using the three low orders EKF. The validation results show that the estimated orientations of the developed AHRS are within the acceptable region, and AHRS can give a stabilized attitude and heading information for a long time.
Highlights
Recent advances in autonomous vehicle technologies have made unmanned aerial vehicles (UAV) to become an attractive solution for the modern military and civilian applications such as aerial surveying, pipeline and power line inspection, post-disaster assessment, remote sensing, and cruise missiles [1, 2]
The real attitude and heading are processed on an embedded processor in actual flying test, major difficulties when implementing a centralized extended Kalman filter (CEKF) on a embedded processor arise from the complexity caused by the need of the Jacobian matrix computing and the system equation linearizing, and this problem is exacerbated by the need to implement an Extended Kalman Filter (EKF) with a large number of states
Experiments are conducted using an autopilot self-developed from Digital Navigation Center (DNC), BUAA
Summary
Recent advances in autonomous vehicle technologies have made unmanned aerial vehicles (UAV) to become an attractive solution for the modern military and civilian applications such as aerial surveying, pipeline and power line inspection, post-disaster assessment, remote sensing, and cruise missiles [1, 2]. The Kalman filter provides the best estimates based on the system dynamics and a priori knowledge of the noise characteristics of the signals. Major difficulties when implementing a centralized Kalman filter (CKF) on a microcontroller arise from the complexity caused by the need for inverting certain matrices. This problem is exacerbated by the need to implement an EKF in case the system is nonlinear and with a large number of states. The attitude and heading estimation filter proposed in this work is based on the distributed filtering theory, initial attitude estimation is based on the complementary filtering theory, and the real-time attitude estimation will use the three low orders EKF for UAV flying.
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