Frequency Estimation Using SAMP-SVD Based on Nyquist Folding Receiver

Abstract

Abstract. 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.

The Nyquist Folding Receiver and Zero-Sum Game

“…The success of the Nyquist Folding Receiver (NYFR) [1] structure in wideband signals processing have inspired investigation of NYFR, which is an efficient analogy-toinformation (A2I) architecture [2], [3] that folds the broadband RF inputs by subsampling so that can sample with a low-speed ADC. The most significant feature of NYFR is sampling with periodically non-uniform [4] that is the key to folding, by resampling structure with a stream of short radio frequency (RF) pulses and a narrowband ADC. In practice, the degree of nonuniformity and the size of period have a great influence on the output of NYFR.…”