Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear mod

A series of computational experiments indicate that the signal acquisition error is minimized when a significant fraction of the CS measurements is allowed to saturate (10–30 in our experiments). This challenges the conventional wisdom of both conven

A series of computational experiments indicate that the signal acquisition error is minimized when a significant fraction of the CS measurements is allowed to saturate (10–30 in our experiments). This challenges the conventional wisdom of both conven