Efficient and Optimal Attitude Determination Using Recursive Global Positioning System Signal Operations

A new and efficient algorithm is developed for attitude determination from global positioning system signals. The new algorithm is derived from a generalized predictive filter for nonlinear systems. This uses a one time-step ahead approach to propagate a simple kinematics model for attitude determination. The advantages of the new algorithm over previously developed methods include: it provides optimal attitudes even for coplanar baseline configurations, it guarantees convergence even for poor initial conditions, it is a noniterative algorithm, and it is computationally efficient. These advantages clearly make the new algorithm well suited to onboard applications. The performance of the new algorithm is tested on a dynamic hardware simulator. Results indicate that the new algorithm accurately estimates the attitude of a moving vehicle and provides robust attitude estimates even when other methods, such as a linearized least-squares approach, fail due to poor initial starting conditions.