A compressive sensing framework for multirate signal estimation

Ekşioğlu, Ender M.; Tanc, A. Korhan; Kayran, A.H.

doi:10.1109/isspa.2010.5605572

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…This parameterization is consistent and at least one optimum synthesis filter bank is guaranteed. We design the synthesis bank for a more general set up in comparison to existing methods [57][58][59][60][61]. The synthesis filter length can be arbitrary but given.…”

Section: A Contributionmentioning

confidence: 99%

“…In [58], Li Chai et al formulated the problem using structural similarity criterion and obtained closed-form solution under certain assumptions like the filter length is equal to the decimation factor, the mean of the source is zero, etc. Compresive sensing based method was used in [59] to estimate a signal from multirate observations. However, the signal has to be sparse.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Signals via Non-Maximally Decimated Non-Uniform Filter Banks

Patel

Dhuli

Lall

2019

IEEE Trans. Circuits Syst. I

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This paper addresses the important problem of reconstructing a signal from multiple multirate observations. The observations are modeled as the output of an analysis bank, and time-domain analysis is carried out to design an optimal FIR synthesis bank. We pose this as a minimizing the mean-square problem and prove that at least one optimal solution is always possible. A parametric form for all optimal solutions is obtained for a non-maximally decimated filter bank. The necessary and sufficient conditions for an optimal solution, that results in perfect reconstruction (PR), are derived as time-domain pseudocirculant conditions. This represents a novel theoretical contribution in multirate filter bank theory. We explore PR in a more general setting. This results in the ability to design a synthesis bank with a particular delay in the reconstruction. Using this delay, one can achieve PR in cases where it might not have been possible otherwise. Further, we extend the design and analysis to nonuniform filter banks and carry out simulations to verify the derived results.

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Section: A Contributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Signals via Non-Maximally Decimated Non-Uniform Filter Banks

Patel

Dhuli

Lall

2019

IEEE Trans. Circuits Syst. I

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“…It is remarked that both ME and Wiener filter based methods require the knowledge of second-order statistics related to the desired HR signal and the noisy LR observations. In addition to these mentioned two main groups of methods, recently, adaptive filtering and compressive sensing based approaches dealing with the statistical multirate signal estimation are suggested [10][11][12].…”

Section: Introductionmentioning

confidence: 99%