Detection Algorithm for Compressive Sampling Signal Using Eigenvalues Energy

According to the Nyquist sampling theorem, sampling rate should not be less than twice the Nyquist sampling rate in order to avoid signal distortion. However, with the increasing use of bandwidth, highspeed sampling rate is beyond the current technology level. Since compressive sampling can effectively maintain the structures and information of sparse signal, detection can be accomplished by directly processing samples without reconstructing the original signal. For the blind detection of compressive sampled signal, an algorithm based on eigenvalues energy was proposed. In this paper, cyclic autocorrelation matrix was firstly analyzed and reconstructed on the condition that cyclic frequency equals zero. After that, detection statistics was structured on the basis of eigenvalue decomposition. With the threshold acquired by analyzing the distribution of detection statistics, detection was finally implemented. Simulation results show that the proposed algorithm has the advantage of better detection performance and lower complexity over current algorithms in same cases.