E-Optimal Sensor Selection for Compressive Sensing-Based Purposes
Collaborative estimation of a sparse vector \mathbf {x} x by M M potential measurements is considered. Each measurement is the projection of \mathbf {x} x obtained by a regressor, i.e., y_m=\mathbf {a}_m^T \mathbf {x} ym=amTx . The problem of selecting K K sensor measurements from a set of M M...
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Veröffentlicht in: | IEEE transactions on big data 2020-03, Vol.6 (1), p.51-65 |
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Sprache: | eng |
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Zusammenfassung: | Collaborative estimation of a sparse vector \mathbf {x} x by M M potential measurements is considered. Each measurement is the projection of \mathbf {x} x obtained by a regressor, i.e., y_m=\mathbf {a}_m^T \mathbf {x} ym=amTx . The problem of selecting K K sensor measurements from a set of M M potential sensors is studied where K\ll M K≪M and K K is less than the dimension of \mathbf {x} x . In other words, we aim to reduce the problem to an under-determined system of equations in which a sparse solution is desired. This paper suggests selecting sensors in a way that the reduced matrix construct a well conditioned measurement matrix. Our criterion is based on E-optimality, which is highl |
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ISSN: | 2332-7790 2372-2096 |
DOI: | 10.1109/TBDATA.2018.2868120 |