Single-snapshot DoA estimation using adaptive elastic net in the complex domain

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

The elastic net (EN) is a popular regularization and variable selection method that overcomes the shortcomings of Lasso such as poor recovery in the face of high mutual coherence. In this paper, we develop an efficient algorithm to solve the weighted EN criterion for complex-valued measurements applying the cyclic coordinate descent approach. We illustrate that usage of smartly chosen adaptive (i.e., data-dependent) weights revamps the algorithm to overcome the shortcomings of naive (non-weighted) EN and enhances the exact recovery. Usefulness of the proposed algorithm is illustrated for compressed beamforming (CBF) with the single-snapshot DoA (direction-of-arrival) estimation, in a demanding multi-source scenario that contains closely spaced sources having large variation in source powers. Accurate DoA's estimation performance and error plummet by the proposed algorithm validates its application and advocates its potential usage in other signal processing problems.

Original languageEnglish
Title of host publication4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016
PublisherIEEE
Pages197-201
Number of pages5
ISBN (Electronic)9781509029204
DOIs
Publication statusPublished - 15 Nov 2016
MoE publication typeA4 Article in a conference publication
EventInternational Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing - Aachen, Germany
Duration: 19 Sep 201622 Sep 2016
Conference number: 4
http://workshops.fhr.fraunhofer.de/cosera/

Workshop

WorkshopInternational Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing
Abbreviated titleCoSeRa
CountryGermany
CityAachen
Period19/09/201622/09/2016
Internet address

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