Linearly constrained estimation via state space decomposition

This paper considers the estimation problem of the linearly constrained state. By combining the auxiliary dynamics and the constraint, the evolution of the constrained state is depicted via state space decomposition techniques. The effects of the incorporated constraints on the observability of the system model are also discussed. Based on different deterministic sampling methods, two algorithms for the linearly constrained state are presented. Finally, the effectiveness of the two algorithms is measured by a numerical simulation.