Today, a number of applications need to process large bandwidth signals. These applications frequently require the use of fast ADCs and very efficient DSP structures that are difficult to design. An interesting solution for facing these issues is the Compressive Sensing (CS) method, which, assuming to know some properties of the signal, allows to reduce the sampling rate well below the Nyquist rate. A negative aspect of CS is the need to introduce an additional element for the reconstruction the sampled signal. This reconstruction requires techniques that generally have an high computational cost, representing a critical element for a real-time implementation of CS systems. In this work we present the implementation of one of these reconstruction algorithms, named Orthogonal Matching Pursuit (OMP). This algorithm involves heavy computational cost (in particular for the matrix computation), which limits its use in the case of a strictly real-time applications, as in the case of radar systems. To overcome this limitation authors propose a solution that uses for the implementation a mixed software/hardware approach. The proposed architecture was implemented on the Xilinx ZYNQ FPGA. The experimental results show a significant speed-up of the algorithm.
Acciarito, S., Cardarilli, G.c., Di Nunzio, L., Fazzolari, R., Khanal, G.m., Re, M. (2018). Compressive sensing reconstruction for complex system: A hardware/software approach. In APPLICATIONS IN ELECTRONICS PERVADING INDUSTRY, ENVIRONMENT AND SOCIETY, APPLEPIES 2016 (pp.192-200). GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : SPRINGER INTERNATIONAL PUBLISHING AG [10.1007/978-3-319-55071-8_25].
Compressive sensing reconstruction for complex system: A hardware/software approach
Acciarito S.;Cardarilli G. C.;Di Nunzio L.;Fazzolari R.;Khanal G. M.;Re Marco.
2018-01-01
Abstract
Today, a number of applications need to process large bandwidth signals. These applications frequently require the use of fast ADCs and very efficient DSP structures that are difficult to design. An interesting solution for facing these issues is the Compressive Sensing (CS) method, which, assuming to know some properties of the signal, allows to reduce the sampling rate well below the Nyquist rate. A negative aspect of CS is the need to introduce an additional element for the reconstruction the sampled signal. This reconstruction requires techniques that generally have an high computational cost, representing a critical element for a real-time implementation of CS systems. In this work we present the implementation of one of these reconstruction algorithms, named Orthogonal Matching Pursuit (OMP). This algorithm involves heavy computational cost (in particular for the matrix computation), which limits its use in the case of a strictly real-time applications, as in the case of radar systems. To overcome this limitation authors propose a solution that uses for the implementation a mixed software/hardware approach. The proposed architecture was implemented on the Xilinx ZYNQ FPGA. The experimental results show a significant speed-up of the algorithm.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
