Scheduling Kalman filters in continuous time
Author(s)Ny, Jerome Le; Feron, Eric; Dahleh, Munther A.
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A set of N independent Gaussian linear time invariant systems is observed by M sensors whose task is to provide the best possible steady-state causal minimum mean square estimate of the state of the systems, in addition to minimizing a steady-state measurement cost. The sensors can switch between systems instantaneously, and there are additional resource constraints, for example on the number of sensors which can observe a given system simultaneously. We first derive a tractable relaxation of the problem, which provides a bound on the achievable performance. This bound can be computed by solving a convex program involving linear matrix inequalities. Exploiting the additional structure of the sites evolving independently, we can decompose this program into coupled smaller dimensional problems. In the scalar case with identical sensors, we give an analytical expression for an index policy proposed in a more general context by Whittle. In the general case, we develop open-loop periodic switching policies whose performance matches the bound arbitrarily closely.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
American Control Conference
Institute of Electrical and Electronics Engineers
Le Ny, J., E. Feron, and M.A. Dahleh. “Scheduling Kalman Filters in Continuous Time.” American Control Conference, 2009. ACC ’09. 2009. 3799-3805. Copyright © 2009, IEEE
Final published version
INSPEC Accession Number: 10775164