Development of an efficient algorithm for computation of time varying sparse signal

Digital era requisites higher resolution signal acquisition, transmission and storage. Thus the new concept of Compressive Sensing, merging the compression and sampling to transmit lesser non zero coefficients so that bandwidth is reduced. The proposed methodology particularly suits for the deterministic construction of sparse signal, utilizing the aposteriori statistics of the Fourier matrix and rearranging the matrix as the compressed sensing matrix and verified for MIP. Also Expectation Maximization algorithm along with the new compressed sensing matrix is incorporated in order to deal with the dynamic sparse signals. Thus the recovery rate can be efficiently improved and analyzed using the Mean Square Error plot provided.