Improved Generalized Sparsity Adaptive Matching Pursuit Algorithm Based on Compressive Sensing

• Main Authors: Zhao Liquan, Ma Ke, Jia Yanfei
• Format: Article
• Language: English
• Published: Hindawi Limited 2020-01-01
• Series: Journal of Electrical and Computer Engineering
• Online Access: http://dx.doi.org/10.1155/2020/2782149

The modified adaptive orthogonal matching pursuit algorithm has a lower convergence speed. To overcome this problem, an improved method with faster convergence speed is proposed. In respect of atomic selection, the proposed method computes the correlation between the measurement matrix and residual and then selects the atoms most related to residual to construct the candidate atomic set. The number of selected atoms is the integral multiple of initial step size. In respect of sparsity estimation, the proposed method introduces the exponential function to sparsity estimation. It uses a larger step size to estimate sparsity at the beginning of iteration to accelerate the algorithm convergence speed and a smaller step size to improve the reconstruction accuracy. Simulations show that the proposed method has better performance in terms of convergence speed and reconstruction accuracy for one-dimension signal and two-dimension signal.