Abstract
The multiple signal classification (MUSIC) algorithm, which was originally proposed to solve the direction of arrival (DOA) estimation problem in sensor array processing, has attracted much attention in recent years as a method to solve the multiple-measurement vectors (MMV) problem. While MUSIC reliably reconstructs the row sparse signals in the full row rank case, it performs poor in the rank deficient case. In order to overcome the limitation of MUSIC, we propose a robust greedy algorithm, henceforth referred to as an MMV orthogonal least squares (MMV-OLS) algorithm, for the MMV problem. Our analysis shows that in the full row rank case, MMV-OLS guarantees exact reconstruction of any row K-sparse signals from K + 1 measurements, which is in fact optimal since K + 1 is the smallest number of measurements to recover the row K-sparse matrices. In addition, we show that the recovery performance of MMV-OLS is competitive even in the rank deficient case by providing empirical results.
| Original language | English |
|---|---|
| Title of host publication | TENCON 2017 - 2017 IEEE Region 10 Conference |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1269-1272 |
| Number of pages | 4 |
| Volume | 2017-December |
| ISBN (Electronic) | 9781509011339 |
| DOIs | |
| Publication status | Published - 2017 Dec 19 |
| Externally published | Yes |
| Event | 2017 IEEE Region 10 Conference, TENCON 2017 - Penang, Malaysia Duration: 2017 Nov 5 → 2017 Nov 8 |
Other
| Other | 2017 IEEE Region 10 Conference, TENCON 2017 |
|---|---|
| Country | Malaysia |
| City | Penang |
| Period | 17/11/5 → 17/11/8 |
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ASJC Scopus subject areas
- Computer Science Applications
- Electrical and Electronic Engineering
Cite this
Orthogonal least squares algorithm for the multiple-measurement vectors problem. / Kim, Junhan; Shim, Byonghyo.
TENCON 2017 - 2017 IEEE Region 10 Conference. Vol. 2017-December Institute of Electrical and Electronics Engineers Inc., 2017. p. 1269-1272.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Orthogonal least squares algorithm for the multiple-measurement vectors problem
AU - Kim, Junhan
AU - Shim, Byonghyo
PY - 2017/12/19
Y1 - 2017/12/19
N2 - The multiple signal classification (MUSIC) algorithm, which was originally proposed to solve the direction of arrival (DOA) estimation problem in sensor array processing, has attracted much attention in recent years as a method to solve the multiple-measurement vectors (MMV) problem. While MUSIC reliably reconstructs the row sparse signals in the full row rank case, it performs poor in the rank deficient case. In order to overcome the limitation of MUSIC, we propose a robust greedy algorithm, henceforth referred to as an MMV orthogonal least squares (MMV-OLS) algorithm, for the MMV problem. Our analysis shows that in the full row rank case, MMV-OLS guarantees exact reconstruction of any row K-sparse signals from K + 1 measurements, which is in fact optimal since K + 1 is the smallest number of measurements to recover the row K-sparse matrices. In addition, we show that the recovery performance of MMV-OLS is competitive even in the rank deficient case by providing empirical results.
AB - The multiple signal classification (MUSIC) algorithm, which was originally proposed to solve the direction of arrival (DOA) estimation problem in sensor array processing, has attracted much attention in recent years as a method to solve the multiple-measurement vectors (MMV) problem. While MUSIC reliably reconstructs the row sparse signals in the full row rank case, it performs poor in the rank deficient case. In order to overcome the limitation of MUSIC, we propose a robust greedy algorithm, henceforth referred to as an MMV orthogonal least squares (MMV-OLS) algorithm, for the MMV problem. Our analysis shows that in the full row rank case, MMV-OLS guarantees exact reconstruction of any row K-sparse signals from K + 1 measurements, which is in fact optimal since K + 1 is the smallest number of measurements to recover the row K-sparse matrices. In addition, we show that the recovery performance of MMV-OLS is competitive even in the rank deficient case by providing empirical results.
UR - http://www.scopus.com/inward/record.url?scp=85044196526&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044196526&partnerID=8YFLogxK
U2 - 10.1109/TENCON.2017.8228052
DO - 10.1109/TENCON.2017.8228052
M3 - Conference contribution
AN - SCOPUS:85044196526
VL - 2017-December
SP - 1269
EP - 1272
BT - TENCON 2017 - 2017 IEEE Region 10 Conference
PB - Institute of Electrical and Electronics Engineers Inc.
ER -