Online compressive covariance sensing

Abstract Several sampling strategies, which compress an analog signal and recover it, are attracting attention. Representatively, compressive sensing (CS) can restore a sparse signal from its compressed one sampled at a lower rate than the Nyquist rate. However, CS is not appropriate to non-sparse signals. Recently developed compressive covariance sensing (CCS) methods have received great attention. The CCS can compress a non-sparse signal and recover its second-order statistics, but previous CCSs are not only batch processes but also assume that a signal is wide sense-stationary (WSS). Hence, advanced CCSs need to be developed. In this study, two CCS methods are proposed. One is an associative array-based CCS, which is also a batch processor but requires a lower computational complexity than the previous CCSs. The other is a novel online CCS that sequentially compresses a signal and recovers its covariance. The online CCS can trace the characteristics of non-stationary signal. Mathematical analysis and simulation results showed that the proposed methods have high covariance tracking performance as well as accurate restoration.

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