Bound on the estimation grid size for sparse reconstruction in direction of arrival estimation

Mario Coutiño; Radmila Pribic; Geert Leus

doi:10.1109/ssp.2016.7551781

Bound on the estimation grid size for sparse reconstruction in direction of arrival estimation

Mario Coutiño, Radmila Pribic, Geert Leus

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

23 Downloads (Pure)

Abstract

A bound for sparse reconstruction involving both the signal-to-noise ratio (SNR) and the estimation grid size is presented. The bound is illustrated for the case of a uniform linear array (ULA). By reducing the number of possible sparse vectors present in the feasible set of a constrained ℓ1-norm minimization problem, ambiguities in the reconstruction of a single source under noise can be reduced. This reduction is achieved by means of a proper selection of the estimation grid, which is naturally linked with the mutual coherence of the sensing matrix. Numerical simulations show the performance of sparse reconstruction with an estimation grid meeting the provided bound demonstrating the effectiveness of the proposed bound.

Original language	English
Title of host publication	2016 IEEE Statistical Signal Processing Workshop (SSP)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1-4
Number of pages	4
ISBN (Print)	978-1-4673-7802-4
DOIs	https://doi.org/10.1109/ssp.2016.7551781
Publication status	Published - 25 Aug 2016
Event	2016 IEEE Statistical Signal Processing Workshop (SSP) - Palma de Mallorca, Spain Duration: 26 Jun 2016 → 29 Jun 2017 http://ssp2016.tsc.uc3m.es/

Workshop

Workshop	2016 IEEE Statistical Signal Processing Workshop (SSP)
Abbreviated title	SSP 2016
Country/Territory	Spain
City	Palma de Mallorca
Period	26/06/16 → 29/06/17
Internet address	http://ssp2016.tsc.uc3m.es/

Keywords

sparse reconstruction
direction of arrival (DOA) estimation
sensing matrix
uniform linear array
compressed sensing

Access to Document

10.1109/ssp.2016.7551781

leus16ssp2Accepted author manuscript, 921 KB

Cite this

@inproceedings{39cec135def34088876ac76763398123,

title = "Bound on the estimation grid size for sparse reconstruction in direction of arrival estimation",

abstract = "A bound for sparse reconstruction involving both the signal-to-noise ratio (SNR) and the estimation grid size is presented. The bound is illustrated for the case of a uniform linear array (ULA). By reducing the number of possible sparse vectors present in the feasible set of a constrained ℓ1-norm minimization problem, ambiguities in the reconstruction of a single source under noise can be reduced. This reduction is achieved by means of a proper selection of the estimation grid, which is naturally linked with the mutual coherence of the sensing matrix. Numerical simulations show the performance of sparse reconstruction with an estimation grid meeting the provided bound demonstrating the effectiveness of the proposed bound.",

keywords = "sparse reconstruction, direction of arrival (DOA) estimation, sensing matrix, uniform linear array, compressed sensing",

author = "Mario Couti{\~n}o and Radmila Pribic and Geert Leus",

year = "2016",

month = aug,

day = "25",

doi = "10.1109/ssp.2016.7551781",

language = "English",

isbn = "978-1-4673-7802-4",

pages = "1--4",

booktitle = "2016 IEEE Statistical Signal Processing Workshop (SSP)",

publisher = "IEEE",

address = "United States",

note = "2016 IEEE Statistical Signal Processing Workshop (SSP), SSP 2016 ; Conference date: 26-06-2016 Through 29-06-2017",

url = "http://ssp2016.tsc.uc3m.es/",

}

TY - GEN

T1 - Bound on the estimation grid size for sparse reconstruction in direction of arrival estimation

AU - Coutiño, Mario

AU - Pribic, Radmila

AU - Leus, Geert

PY - 2016/8/25

Y1 - 2016/8/25

N2 - A bound for sparse reconstruction involving both the signal-to-noise ratio (SNR) and the estimation grid size is presented. The bound is illustrated for the case of a uniform linear array (ULA). By reducing the number of possible sparse vectors present in the feasible set of a constrained ℓ1-norm minimization problem, ambiguities in the reconstruction of a single source under noise can be reduced. This reduction is achieved by means of a proper selection of the estimation grid, which is naturally linked with the mutual coherence of the sensing matrix. Numerical simulations show the performance of sparse reconstruction with an estimation grid meeting the provided bound demonstrating the effectiveness of the proposed bound.

AB - A bound for sparse reconstruction involving both the signal-to-noise ratio (SNR) and the estimation grid size is presented. The bound is illustrated for the case of a uniform linear array (ULA). By reducing the number of possible sparse vectors present in the feasible set of a constrained ℓ1-norm minimization problem, ambiguities in the reconstruction of a single source under noise can be reduced. This reduction is achieved by means of a proper selection of the estimation grid, which is naturally linked with the mutual coherence of the sensing matrix. Numerical simulations show the performance of sparse reconstruction with an estimation grid meeting the provided bound demonstrating the effectiveness of the proposed bound.

KW - sparse reconstruction

KW - direction of arrival (DOA) estimation

KW - sensing matrix

KW - uniform linear array

KW - compressed sensing

UR - http://resolver.tudelft.nl/uuid:39cec135-def3-4088-876a-c76763398123

U2 - 10.1109/ssp.2016.7551781

DO - 10.1109/ssp.2016.7551781

M3 - Conference contribution

SN - 978-1-4673-7802-4

SP - 1

EP - 4

BT - 2016 IEEE Statistical Signal Processing Workshop (SSP)

PB - IEEE

CY - Piscataway, NJ

T2 - 2016 IEEE Statistical Signal Processing Workshop (SSP)

Y2 - 26 June 2016 through 29 June 2017

ER -

Bound on the estimation grid size for sparse reconstruction in direction of arrival estimation

Abstract

Workshop

Keywords

Access to Document

Other files and links

Fingerprint

Cite this