Gridless methods for underdetermined source estimation

Author

Pal, Piya ; Vaidyanathan, P.P.

Author_Institution

Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA, USA

fYear

2014

Firstpage

111

Lastpage

115

Abstract

The performances of two gridless algorithms for direction of arrival estimation are analyzed, when the number of sources can be larger than the number of array elements. One of these algorithms is a hybrid scheme recently proposed by the authors that uses low rank recovery techniques, and the other is based on total variation (TV) norm minimization scheme. It is shown that when a nested sensor array is used, and the source signals are assumed to be Gaussian, these recovery algorithms can recover O(M²) sources using M sensors with overwhelming probability in the number of time snapshots.

Keywords

Gaussian processes; array signal processing; direction-of-arrival estimation; minimisation; signal restoration; Gaussian source signals; M sensors; O(M²) source recovery; TV norm minimization scheme; array elements; direction-of-arrival estimation; gridless method; low-rank recovery technique; nested sensor array; overwhelming probability; time snapshots; total variation norm minimization scheme; underdetermined source estimation; Arrays; Covariance matrices; Direction-of-arrival estimation; Estimation; Minimization; Multiple signal classification; Signal processing algorithms; DOA estimation; Low rank matrix recovery; MUSIC; nested and coprime arrays; nuclear norm minimization; super resolution;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2014 48th Asilomar Conference on

Print_ISBN

978-1-4799-8295-0

Type

conf

DOI

10.1109/ACSSC.2014.7094408

Filename

7094408