Abstract
This paper describes a practical and reliable algorithm for implementing an Attitude and Heading Reference System (AHRS). This kind of system is essential for real time vehicle navigation, guidance and control applications. When low cost sensors are used, efficient and robust algorithms are required for performance to be acceptable. The proposed method is based on an Extended Kalman Filter (EKF) in a direct configuration. In this case, the filter is explicitly derived from both the kinematic and error models. The selection of this kind of EKF configuration can help in ensuring a tight integration of the method for its use in filter-based localization and mapping systems in autonomous vehicles. Experiments with real data show that the proposed method is able to maintain an accurate and drift-free attitude and heading estimation. An additional result is to show that there is no ostensible reason for preferring that the filter have an indirect configuration over a direct configuration for implementing an AHRS system.
Highlights
The orientation of a vehicle in space is often referred to as Attitude
This paper considerably extends the authors' previous work [19] where the idea of an AHRS based on an Extended Kalman Filter (EKF) in a direct configuration is introduced
The output estimated by the proposed algorithm (Direct method) is compared with the output obtained from the method described in [11] and [23], which is based on an EKF in indirect formulation (Indirect method)
Summary
The orientation of a vehicle in space is often referred to as Attitude. A combination of instruments capable of maintaining an accurate estimate of the vehicle attitude, while it manoeuvres, is called an AHRS (Attitude and Heading Reference System). An AHRS is a fundamental prerequisite for addressing several navigation and control problems. The first implementations of AHRS were based only on gyroscopes. Gyros are prone to bias, which could produce large errors after long periods of integration. This fact meant that attitude estimation was limited to very expensive applications because sensors with long term bias stability are very expensive, even
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