A Correlation Coefficient Sparsity Adaptive Matching Pursuit Algorithm

Abstract

This paper presents a correlation coefficient sparsity adaptive matching pursuit (CCSAMP) algorithm for practical compressed sensing (CS). The sparsity adaptive matching pursuit (SAMP) has been enhanced using the CCSAMP algorithm. The CCSAMP's capacity to accurately reconstruct the signal with fewer repetitions is its most novel characteristic when compared to other state-of-the-art SAMP enhancement methods. This makes it a candidate for many practical applications that need fast reconstruction. The proposed algorithm constructs two correlation vectors, which represent the input signals recovered from the support set and candidate set. The step size is transformed by their Pearson correlation coefficients (PCCS). Compared to the residual energy, the correlation coefficient is more sensitive. The CCSAMP reduces the number of iterations while maintaining the SAMP's capability of signal reconstruction without prior knowledge of the sparsity. Simulation shows that the CCSAMP can significantly reduce the number of iterations compared to the SAMP algorithm. The CCSAMP can be used for radar detection, radar 3D imaging, and other fields where fast and accurate reconstruction of signals is required.