Adaptive measurement matrix of compressed sensing

Sparse representation, incorrelate projection and reconstruction are the three elements of compressed sensing. The adaptive measurement matrix in this paper uses gaussian random matrix as original matrix, and make an new measurement matrix under the partial positional information of sparse coefficients. When the compressed sensing matrix projects the sparse coefficients, the small coefficients are more close to zero. At the same time, we reduce the measured values by reducing the columns of measurement matrix, as a result, the numbers of data transmission applied adaptive measurement matrix and Gaussian random measurement are nearly. The improved performance of compressed sensing employed adaptive measurement matrix embodies in the reconstruction accuracy, when we use IHT as reconstruction algorithm, both theory and experiment verify the performance of adaptive measurement matrix better than gaussian random measurement matrix.