It is well-known that in a canonical inverse scattering problem there is only a limited amount of independent data which is quantified by the degree of freedom of the problem one is dealing with. Of course, it is mandatory, in order to recover the signal in a proper way, to sample the data at least at Nyquist rate. However, there are some cases in which the number of independent data of a signal is much smaller than what its bandwidth seems to suggest, and in these situations the theory of Compressive Sensing may help to improve reconstruction capabilities without using so much information. In this framework, the following communication deals with a preliminary analysis of the solution of microwave imaging problems by exploiting the theory of Compressive Sensing in a linearized multiview-multistatic single-frequency approach.
A compressive sensing based approach for microwave tomography and GPR applications
AMBROSANIO, MICHELE
;AUTIERI, ROBERTA;PASCAZIO, Vito
2014-01-01
Abstract
It is well-known that in a canonical inverse scattering problem there is only a limited amount of independent data which is quantified by the degree of freedom of the problem one is dealing with. Of course, it is mandatory, in order to recover the signal in a proper way, to sample the data at least at Nyquist rate. However, there are some cases in which the number of independent data of a signal is much smaller than what its bandwidth seems to suggest, and in these situations the theory of Compressive Sensing may help to improve reconstruction capabilities without using so much information. In this framework, the following communication deals with a preliminary analysis of the solution of microwave imaging problems by exploiting the theory of Compressive Sensing in a linearized multiview-multistatic single-frequency approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
