A Robust Null Space Method for Linear Equality Constrained State Estimation

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 resultin...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2010-08, Vol.58 (8), p.3961-3971
Main Authors: Hewett, Russell J, Heath, Michael T, Butala, Mark D, Kamalabadi, Farzad
Format: Article
Language:English
Subjects:
Algorithms
Computer science
Constraint optimization
Constraints
Estimation
Estimators
Factorization
Kalman filtering
linear equality constraints
Mathematical models
Null space
Projection
Remote sensing
Robustness
Space heating
State estimation
State-space methods
Statistics
Stochastic processes
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Summary: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.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2010.2048901