Kalman Filtering in the Presence of State Space Equality Constraints

We discuss two separate techniques for Kalman Filtering in the presence of state space equality constraints. We then prove that despite the lack of similarity in their formulations, under certain conditions, the two methods result in mathematically equivalent constrained estimate structures. We conclude that the potential benefits of using equality constraints in Kalman Filtering often outweigh the computational costs, and as such, equality constraints, when present, should be enforced by way of one of these two methods.

💡 Research Summary

The paper investigates two distinct approaches for incorporating linear equality constraints into the standard Kalman filter framework and demonstrates that, under appropriate conditions, both approaches yield mathematically equivalent constrained estimates. The authors begin by recalling the classic discrete‑time state‑space model

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