A review of the compressive sampling framework in the lights of spherical harmonics: applications to distributed spherical arrays
Bruno Masiero and Martin Pollow
Compressive Sampling proposes a new framework on how to effectively sample information (signals or any other physical phenomena) with a reduced number of sensors. The main idea behind this concept is that if the information to be sampled can be sparsely described in a space that is incoherent to the measurement space, then this information can be restored by minimization. In this paper we describe the Compressive Sampling framework and present one example of application, namely, to sample an outgoing acoustic field with a distributed spherical array composed of a reduced number of sensing microphones without suffering from aliasing errors.