LAUSR

In this paper, we consider the problem of analog-to-information conversion for nonstationary signals, which exhibit time-varying properties with respect to spectral contents. Nowadays, sampling for nonstationary signals is mainly based on Nyquist sampling theorem or signal-dependent techniques. Unfortunately, in the context of the efficient ‘blind’ sampling, these methods are infeasible. To deal with this problem, we propose a novel analog-to-information conversion architecture to achieve the sub-Nyquist sampling for nonstationary signals. With the proposed scheme, we present a multi-channel sampling system to sample the signals in time-frequency domain. We analyze the sampling process and establish the reconstruction model for recovering the original signals. To guarantee the wide application, we establish the completeness under the frame theory. Besides, we provide the feasible approach to simplify the system construction. The reconstruction error for the proposed system is analyzed. We show that, with the consideration of noises and mismatch, the total error is bounded. The effectiveness of the proposed system is verified in the numerical experiments. It is shown that our proposed scheme outperforms the other sampling methods state-of-the-art.