Abstract
State covariances of linear systems satisfy certain constraints imposed by the underlying dynamics. These constraints dictate a particular structure of state covariances. However, sample covariances almost always fail to have the required structure. The renewed interest in using state covariances for estimating the power spectra of inputs gives rise to the approximation problem. In this note, the structured covariance least-squares problem is formulated and the Lyapunov-type matricial linear constraint is converted into an equivalent set of trace constraints. Efficient unconstrained maximization methods capable of solving the corresponding dual problem are developed.
Original language | English (US) |
---|---|
Pages (from-to) | 1643-1648 |
Number of pages | 6 |
Journal | IEEE Transactions on Automatic Control |
Volume | 54 |
Issue number | 7 |
DOIs | |
State | Published - 2009 |
Bibliographical note
Copyright:Copyright 2009 Elsevier B.V., All rights reserved.
Keywords
- Convex optimization
- Least-squares approximation
- Structured covariances