Analysis of the Matching Pursuit Reconstruction Algorithm Based on Compression Sensing

We introduce the concepts of compression sensing and signal reconstruction, and then explained the minimum 0 norm and minimum l1 norm reconstruction algorithms. We extensively study existing reconstruction algorithms and take the advantages of existing algorithms to propose a new reconstruction algorithm. The normalized random Gaussian matrix is used as the measurement matrix. We chose three different sparse signals for comparisons, namely the three classic CS inputs including the time domain sparse signal, the frequency domain compressible signal and the sparsity unknown frequency domain compressible signal. Finally, a series of simulation results show that the proposed algorithm can achieve signal reconstruction with high probability and high precision.

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