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
Hauptverfasser: Joneidi, Mohsen, Zaeemzadeh, Alireza, Shahrasbi, Behzad, Qi, Guo-Jun, Rahnavard, Nazanin
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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
ISSN:2332-7790
2372-2096
DOI:10.1109/TBDATA.2018.2868120