Signal sparsity is directly related to the determination of sampling rate and the construction of measurement matrix in compressive sensing.However

the sparsity order is often unknown or time-varying.In this context

investigating blind sparsity order estimation (SOE) techniques is an open research issue.To address this

asymptotic random matrix spectrum analysis theory was used to derive the asymptotic eigenvalue probability distribution function (AEPDF) of the measured signal's covariance matrix.Then

the paper used the relation between the measurement energy and AEPDF to further deduce the corresponding relation between the sparsity order

compressive rate

SNR and the measured signal energy.Subsequently

based on this relation

a technique to estimate the sparsity order using the measured signal energy was proposed.Simulation results show that the proposed algorithm can gain higher estimation performance with lower computational complexity compared with the existing algorithm.And the estimation accuracy can be enhanced by increasing the sampling overhead.