Graduate School and Research Center in Digital Sciences

A newton-type forward backward greedy method for multi-Snapshot compressed sensing

Bazzi, Ahmad; Slock, Dirk T.M; Meilhac, Lisa

ASILOMAR 2017, Asilomar Conference on Signals, Systels, and Computers, October 29th-November 1st, 2017, Pacific Grove, CA, USA

Parameter estimation has applications in many applications of signal processing, such as Angle-of-Arrival (AoA) estimation. Compressed sensing is a widely growing paradigm that can be applied to parameter estimation via sparse recovery. In this paper, we propose a Newton-type Forward Backward Greedy method that performs sparse recovery, given the observed data over multiple snapshots. This method is applied to the AoA estimation problem, where we have observed better performance, in terms of Mean-Squared Error and faster convergence when compared to existing methods. More information can be found in the conclusions section.

Document Doi Bibtex

Title:A newton-type forward backward greedy method for multi-Snapshot compressed sensing
Type:Conference
Language:English
City:Pacific Grove
Country:UNITED STATES
Date:
Department:Communication systems
Eurecom ref:5272
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Bibtex: @inproceedings{EURECOM+5272, doi = {http://dx.doi.org/10.1109/ACSSC.2017.8335537}, year = {2017}, title = {{A} newton-type forward backward greedy method for multi-{S}napshot compressed sensing}, author = {{B}azzi, {A}hmad and {S}lock, {D}irk {T}.{M} and {M}eilhac, {L}isa}, booktitle = {{ASILOMAR} 2017, {A}silomar {C}onference on {S}ignals, {S}ystels, and {C}omputers, {O}ctober 29th-{N}ovember 1st, 2017, {P}acific {G}rove, {CA}, {USA}}, address = {{P}acific {G}rove, {UNITED} {STATES}}, month = {10}, url = {http://www.eurecom.fr/publication/5272} }
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