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Compressive covariance sensing (CCS) can recover the second-order statistics of a signal that has undergone compression, and this can be achieved without the requirement of sparsity conditions. Instead, certain structure information in the statistical domain is to be captured during compression. In particular, least squares compressive covariance sensing is considered which requires the estimation of the covariance of the available compressed signal in order to recover the covariance matrix of the original signal. Different covariance estimation methods are applied and the CCS performance compared, in the presence of white Gaussian noise, in terms of the normalized mean square error between the true and recovered covariance.

Investigation of the Effect of Different Covariance Estimation Methods on the Performance of Least Squares Compressive Covariance Sensing