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 |
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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 |
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Country | Malaysia |
City | Penang |
Period | 17/11/5 → 17/11/8 |
ASJC Scopus subject areas
- Computer Science Applications
- Electrical and Electronic Engineering