  As technology grows, higher frequency signals are required to be processed in
various applications. In order to digitize such signals, conventional analog to
digital convertors are facing implementation challenges due to the higher
sampling rates. Hence, lower sampling rates (i.e., sub-Nyquist) are considered
to be cost efficient. A well-known approach is to consider sparse signals that
have fewer nonzero frequency components compared to the highest frequency
component. For the prior knowledge of the sparse positions, well-established
methods already exist. However, there are applications where such information
is not available. For such cases, a number of approaches have recently been
proposed. In this paper, we propose several random sampling recovery algorithms
which do not require any anti-aliasing filter. Moreover, we offer certain
conditions under which these recovery techniques converge to the signal.
Finally, we also confirm the performance of the above methods through extensive
simulations.
