This paper deals with the problem of estimating the Directions of Arrival (DOA) of multiple source signals from a single observation of an array data. In particular, an estimation algorithm based on the emerging theory of Compressed Sensing (CS) is analyzed and its statistical properties are investigated. We show that, unlike the classical Fourier beamformer, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g. Capon and MUSIC). Particular attention will be devoted to the super-resolution property. Theoretical arguments and simulation analysis are provided in order to prove that the CSB can achieve a resolution below the classical Rayleigh limit.
Single Snapshot DOA Estimation Using Compressed Sensing
GINI, FULVIO;GRECO, MARIA
2014-01-01
Abstract
This paper deals with the problem of estimating the Directions of Arrival (DOA) of multiple source signals from a single observation of an array data. In particular, an estimation algorithm based on the emerging theory of Compressed Sensing (CS) is analyzed and its statistical properties are investigated. We show that, unlike the classical Fourier beamformer, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g. Capon and MUSIC). Particular attention will be devoted to the super-resolution property. Theoretical arguments and simulation analysis are provided in order to prove that the CSB can achieve a resolution below the classical Rayleigh limit.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
