Sequential sensing with model mismatch

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 measuremen...

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Vydané v:Proceedings / IEEE International Symposium on Information Theory s. 1650 - 1654
Hlavní autori: Song, Ruiyang, Xie, Yao, Pokutta, Sebastian
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2015
Predmet:
Compressed sensing
Covariance
Covariance matrices
Detection
Eigenvalues
Eigenvalues and eigenfunctions
Eigenvectors
Entropy
Estimates
Gaussian
high-dimensional statistics
Information theory
Mutual information
Noise measurement
Sensors
sequential methods
sketching algorithms
ISSN:2157-8095, 2157-8117
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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 equation is used, in terms of the gap of signal posterior entropy, as well as the additional amount of power required to achieve the same signal recovery precision. Based on this, we further study how to choose an initialization for Info-Greedy Sensing using the sample covariance matrix, or using an efficient covariance sketching scheme.
AbstractList 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 capital sigma in a decreasing order of eigenvalues. We establish a set of performance bounds when a mismatched covariance matrix equation is used, in terms of the gap of signal posterior entropy, as well as the additional amount of power required to achieve the same signal recovery precision. Based on this, we further study how to choose an initialization for Info-Greedy Sensing using the sample covariance matrix, or using an efficient covariance sketching scheme.
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 equation is used, in terms of the gap of signal posterior entropy, as well as the additional amount of power required to achieve the same signal recovery precision. Based on this, we further study how to choose an initialization for Info-Greedy Sensing using the sample covariance matrix, or using an efficient covariance sketching scheme.
Author Xie, Yao
Pokutta, Sebastian
Song, Ruiyang
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Snippet We characterize the performance of sequential information guided sensing, Info-Greedy Sensing [1], when there is a mismatch between the true signal model and...
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SubjectTerms Compressed sensing
Covariance
Covariance matrices
Detection
Eigenvalues
Eigenvalues and eigenfunctions
Eigenvectors
Entropy
Estimates
Gaussian
high-dimensional statistics
Information theory
Mutual information
Noise measurement
Sensors
sequential methods
sketching algorithms
Title Sequential sensing with model mismatch
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