A robust null space method for linear equality constrained state estimation

Authors:
Russell J. Hewett;Michael T. Heath;Mark D. Butala;Farzad Kamalabadi
Affiliations:
Department of Computer Science and the Remote Sensing and Space Sciences Group, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL;Remote Sensing and Space Sciences Group, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL;Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
IEEE Transactions on Signal Processing
Year:
2010

Citing 3
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Brief paper: State estimation for linear systems with state equality constraints

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Abstract

We present a robust null space method for linear equality constrained state space estimation. Exploiting a degeneracy in the estimator statistics, an orthogonal factorization is used to decompose the problem into stochastic and deterministic components, which are then solved separately. The resulting dimension reduction algorithm has enhanced numerical stability, solves the constrained problem completely, and can reduce computational load by reducing the problem size. The new method addresses deficiencies in commonly used pseudo-observation or projection methods, which either do not solve the constrained problem completely or have unstable numerical implementations, due in part to the degeneracy in the estimator statistics. We present a numerical example demonstrating the effectiveness of the new method compared to other current methods.