Direction of arrival estimation using robust complex Lasso

Esa Ollila*

*Corresponding author for this work

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

7 Citations (Scopus)


The Lasso (Least Absolute Shrinkage and Selection Operator) has been a popular technique for simultaneous linear regression estimation and variable selection. In this paper, we propose a new novel approach for robust Lasso that follows the spirit of M-estimation. We define M-Lasso estimates of regression and scale as solutions to generalized zero sub-gradient equations. Another unique feature of this paper is that we consider complex-valued measurements and regression parameters, which requires careful mathematical characterization of the problem. An explicit and efficient algorithm for computing the M-Lasso solution is proposed that has comparable computational complexity as state-of-the-art algorithm for computing the Lasso solution. Usefulness of the M-Lasso method is illustrated for direction-of-arrival (DoA) estimation with sensor arrays in a single snapshot case.

Original languageEnglish
Title of host publication2016 10th European Conference on Antennas and Propagation, EuCAP 2016
ISBN (Electronic)9788890701863
Publication statusPublished - 31 May 2016
MoE publication typeA4 Article in a conference publication
EventEuropean Conference on Antennas and Propagation - Davos, Switzerland
Duration: 10 Apr 201615 Apr 2016
Conference number: 10

Publication series

NameProceedings of the European Conference on Antennas and Propagation
ISSN (Electronic)2164-3342


ConferenceEuropean Conference on Antennas and Propagation
Abbreviated titleEuCAP


  • beamforming
  • Compressive sensing
  • DoA estimation
  • Lasso
  • sparsity


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