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2010
IEEE

On the observability of linear systems from random, compressive measurements

8 years 2 months ago
On the observability of linear systems from random, compressive measurements
Abstract-- Recovering or estimating the initial state of a highdimensional system can require a potentially large number of measurements. In this paper, we explain how this burden can be significantly reduced for certain linear systems when randomized measurement operators are employed. Our work builds upon recent results from the field of Compressive Sensing (CS), in which a high-dimensional signal containing few nonzero entries can be efficiently recovered from a small number of random measurements. In particular, we develop concentration of measure bounds for the observability matrix and explain circumstances under which this matrix can satisfy the Restricted Isometry Property (RIP), which is central to much analysis in CS. We also illustrate our results with a simple case study of a diffusion system. Aside from permitting recovery of sparse initial states, our analysis has potential applications in solving inference problems such as detection and classification of more general init...
Michael B. Wakin, Borhan Molazem Sanandaji, Tyrone
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where CDC
Authors Michael B. Wakin, Borhan Molazem Sanandaji, Tyrone L. Vincent
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