2015 IEEE International Symposium on Information Theory (ISIT) 2015
DOI: 10.1109/isit.2015.7282736
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Sequential sensing with model mismatch

Abstract: We characterize the performance of sequential information guided sensing, Info-Greedy Sensing [1], when there is a mismatch between the true signal model and the assumed model, which may be a sample estimate. In particular, we consider a setup where the signal is low-rank Gaussian and the measurements are taken in the directions of eigenvectors of the covariance matrix Σ in a decreasing order of eigenvalues. We establish a set of performance bounds when a mismatched covariance matrix Σ is used, in terms of the… Show more

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