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The Nyquist Folding Receiver (NYFR) is an efficient analogy-to-information (A2I) architecture that folds the broadband RF inputs by subsampling so that can sample with a low-speed ADC. The compressive sensing (CS) model of the NYFR can be built in the sparse environment to recover by traditional CS algorithms. This paper presents an improved algorithm SAMP-SVD based on sparsity adaptive matching pursuit (SAMP) and singular value decomposition (SVD) which is intended to deal with the problem caused by SAMP recovering—the inversion of matrix, avoiding the problem of whether matrix is singular and reducing the time of computation. A comparative analysis between the presented method and sparse reconstruction by separable approximation (SpaRSA) is discussed; simulation results show the algorithm can reconstruct the signal effectively and estimate the frequencies accurately

Frequency Estimation Using SAMP-SVD Based on Nyquist Folding Receiver