Direction of arrival estimation using robust complex Lasso

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Direction of arrival estimation using robust complex Lasso. / Ollila, Esa.

2016 10th European Conference on Antennas and Propagation, EuCAP 2016. IEEE, 2016. 7481141 (Proceedings of the European Conference on Antennas and Propagation).

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

Harvard

Ollila, E 2016, Direction of arrival estimation using robust complex Lasso. in 2016 10th European Conference on Antennas and Propagation, EuCAP 2016., 7481141, Proceedings of the European Conference on Antennas and Propagation, IEEE, European Conference on Antennas and Propagation, Davos, Switzerland, 10/04/2016. https://doi.org/10.1109/EuCAP.2016.7481141

APA

Ollila, E. (2016). Direction of arrival estimation using robust complex Lasso. In 2016 10th European Conference on Antennas and Propagation, EuCAP 2016 [7481141] (Proceedings of the European Conference on Antennas and Propagation). IEEE. https://doi.org/10.1109/EuCAP.2016.7481141

Vancouver

Ollila E. Direction of arrival estimation using robust complex Lasso. In 2016 10th European Conference on Antennas and Propagation, EuCAP 2016. IEEE. 2016. 7481141. (Proceedings of the European Conference on Antennas and Propagation). https://doi.org/10.1109/EuCAP.2016.7481141

Author

Ollila, Esa. / Direction of arrival estimation using robust complex Lasso. 2016 10th European Conference on Antennas and Propagation, EuCAP 2016. IEEE, 2016. (Proceedings of the European Conference on Antennas and Propagation).

Bibtex - Download

@inproceedings{e7174aa9d72246388d3298336274f379,
title = "Direction of arrival estimation using robust complex Lasso",
abstract = "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.",
keywords = "beamforming, Compressive sensing, DoA estimation, Lasso, sparsity",
author = "Esa Ollila",
year = "2016",
month = "5",
day = "31",
doi = "10.1109/EuCAP.2016.7481141",
language = "English",
series = "Proceedings of the European Conference on Antennas and Propagation",
publisher = "IEEE",
booktitle = "2016 10th European Conference on Antennas and Propagation, EuCAP 2016",
address = "United States",

}

RIS - Download

TY - GEN

T1 - Direction of arrival estimation using robust complex Lasso

AU - Ollila, Esa

PY - 2016/5/31

Y1 - 2016/5/31

N2 - 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.

AB - 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.

KW - beamforming

KW - Compressive sensing

KW - DoA estimation

KW - Lasso

KW - sparsity

UR - http://www.scopus.com/inward/record.url?scp=84979300020&partnerID=8YFLogxK

U2 - 10.1109/EuCAP.2016.7481141

DO - 10.1109/EuCAP.2016.7481141

M3 - Conference contribution

AN - SCOPUS:84979300020

T3 - Proceedings of the European Conference on Antennas and Propagation

BT - 2016 10th European Conference on Antennas and Propagation, EuCAP 2016

PB - IEEE

ER -

ID: 6762308