Computationally efficient GES cascade observer for attitude estimation

This paper presents the design, analysis, and performance evaluation of a novel cascade observer for attitude estimation. First, a sensor-based observer, which resorts to rate gyro readings and a set of vector observations, estimates the rate gyro bias. Afterwards, a second observer explicitly estimates the attitude in the form of a rotation matrix based on the rate gyro measurements, the vector observations, and the estimate of the rate gyro bias provided by the first observer. The error dynamics of the overall cascade estimation system are globally exponentially stable (GES) and do not suffer from drawbacks common to attitude estimation solutions such as singularities, unwinding phenomena, or topological limitations for achieving global asymptotic stability (GAS). In addition, the proposed system is computationally efficient and hence it is easily implementable with low computational capabilities. The fact that the observer does not evolve explicitly on SO(3), providing in fact estimates that converge asymptotically to SO(3), is also addressed and an effective and efficient solution is proposed. Finally, the resulting estimator is evaluated, where a Motion Rate Table (MRT) that provides ground truth data is employed for performance evaluation purposes.