Signal Reconstruction Algorithm Application Research under Compressed Sensing in Sparse Signal Reconstruction

Abstract

To improve the efficiency of compressed sensing sparse signal reconstruction, a reconstruction algorithm suitable for different scenarios is proposed. On the basis of greedy algorithm, a sparse reconstruction algorithm for optimization is constructed. A multi-source sparse signal reconstruction algorithm with improved support set estimation is proposed. Experimental data show that the mean square error of the optimized sparse signal reconstruction algorithm is less than 10-5, which is 1-4 orders of magnitude smaller than other comparative algorithms (suh as orthogonal matching pursuit). The support set estimation accuracy of the joint sparse signal reconstruction algorithm is the highest. When the signal-to-noise ratio is 10, the relative reconstruction error based on the orthogonal matching tracking algorithm is 0.57. The minimum relative reconstruction error of the proposed joint sparse signal reconstruction algorithm is 0.34. The analysis of experimental data shows that the decentralized joint sparse signal reconstruction algorithm proposed in this paper not only ensures the efficiency of signal reconstruction, but also reduces the computational complexity.