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Sparse TFD Reconstruction Using the Information on the Instantaneous Number of Signal Components

Over the last few years, signal sparsity has become a popular topic of research in the field of signal processing, exploiting the fact that the heavily undersampled signal can be successfully reconstructed if the considered signal has a sparse representation. The time- frequency (TF) domain often induces sparsity in many real-life signals, hence allowing application of the compressive sensing (CS) based methods. Utilization of the CS based methods achieves better interference suppression and signal concentration enhancement when compared to the traditional TF signal processing methods ; both achieved through the sparse reconstruction algorithm. In this paper, we propose a sparse TF distribution (TFD) reconstruction algorithm which utilizes the information on the instantaneous number of signal components in order to hard-threshold each TFD time slice independently. The performance of the proposed algorithm has been compared to the state-of-the-art sparse reconstruction algorithms in terms of the resulting sparse TFD concentration measure and the algorithms execution time.

Signal sparsity, Compressive sensing, Time-frequency distributions, Ambiguity function, l0-norm, Renyi entropy, Signal reconstruction.

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