Three-axis attitude estimation using rate-integrating gyroscopes

Traditionally, attitude estimation has been performed using a combination of external attitude sensors and internal three-axis gyroscopes. There are many studies of three-axis attitude estimation using gyros that read angular rates. Rate-integrating gyros measure integrated rates or angular displacements, but three-axis attitude estimation using these types of gyros has not been as fully investigated. This paper derives a Kalman filtering framework for attitude estimation using attitude sensors coupled with rate-integrating gyroscopes. In order to account for correlations introduced by using these gyros, the state vector must be augmented, compared with filters using traditional gyros that read angular rates. Two filters are derived in this paper. The first uses an augmented state-vector form that estimates attitude, gyro biases, and gyro angular displacements. The second ignores correlations, leading to a filter that estimates attitude and gyro biases only. Simulation comparisons are shown for both filters. The work presented in this paper focuses only on attitude estimation using rate-integrating gyros, but it can easily be extended to other applications such as inertial navigation, which estimates attitude and position.