  Compressive sensing has been receiving a great deal of interest from
researchers in many areas because of its ability in speeding up data
acquisition. This framework allows fast signal acquisition and compression when
signals are sparse in some domains. It extracts the main information from high
dimensional sparse signals using only a few samples, then the sparse signals
are recovered from the few measurements. There are two main points to consider
when it comes to using compressive sensing. The first one is how to design the
linear measurement matrix to ensure that the compressive sensing is meeting the
objectives of the application. The second is how to recover the sparse signal
from few measurements. Performing compressive sensing requires analyzing and
investigating the efficiency of the measurement matrix and the recovery
algorithm. To date, constructing explicit measurement matrices and developing
efficient recovery algorithms are still open challenges in applications. Thus,
this paper describes metrics to evaluate the performance of compressive sensing
techniques.
