engleski

Adaptive thresholding Scheme for the L1-norm Based Time-Frequency Domain Reconstruction

Time-frequency (TF) signal representation allows observation of the signal features which are often not accessible in the time and frequency domain. However, the joint TF domain introduces the unwanted artifacts, making the TF interpretation more challenging, thus making the TF domain less appealing in the broad applications. Recently, a method based on the sparsity constraint has been introduced to the TF signal processing, achieving artifact suppression by exploiting the fact that most real-life signals are sparse in the TF domain. In this paper, we combine the fast intersection of confidence intervals rule with the two- step iterative shrinkage/thresholding algorithm, and apply it to the L1-norm based sparse TF distribution reconstruction. The performance of the proposed algorithm is compared to the currently available state-of-the-art sparse reconstruction algorithms in terms of the TF concentration measure and the algorithm execution time.

Sparse time–frequency distributions ; Ambiguity function ; Compressive sensing ; Fast intersection of confidence intervals (FICI) rule

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