Reconstruction of Non-stationary Signals With Missing Samples Using S-method and a Gradient Based Reconstruction Algorithm
Keywords:
Compressive sensing, Digital signal processing, Gradient algorithm, Sparse Signal processing, S-methodAbstract
This paper addresses the reconstruction problem of non-stationary signals with missing samples. The reconstruction is achieved by using concentration measures of time-frequency representations in combination with a gradient-based iterative algorithm. As an example of time-frequency representation, the S-method is used in the proposed approach. The sparsity of the transform domain, needed for a successful reconstruction, is interpreted through the concept of concentration measures, and limits for successful reconstruction are discussed. Several examples with nonstationary signals which exhibit different concentrations in the time-frequency
domain illustrate the presented theoretical concepts.
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