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 aremany studies of three-axis attitude estimation using gyroscopes that read angular rates. Rate-integrating gyroscopesmeasure integrated rates or angular displacements, but three-axis attitude estimation using these types of gyroscopes has not been as fully investigated.This paper derives aKalman filtering framework for attitude estimation using attitude sensors coupled with rate-integrating gyroscopes. To account for correlations introduced by using these gyroscopes, the state vectormust be augmented, comparedwith filters using traditional gyroscopes that read angular rates.Two filters are derived in this paper.The first uses an augmented state-vector formthat estimates attitude, gyroscope biases, andgyroscope angular displacements.The secondignores correlations, leading to a filter that estimates attitude and gyroscope biases only. Simulation comparisons are shown for both filters. Thework presented in this paper focuses only on attitude estimation using rate-integrating gyroscopes, but it can easily be extended to other applications such as inertial navigation, which estimates attitude and position.