Sparse Methods for Direction-of-Arrival Estimation
By Zai Yang, Jian Li, P. Stoica, Lihua Xie. arXiv.org, 2016
298 citations recorded by the source corpus.
Abstract Direction-of-arrival (DOA) estimation refers to the process of retrieving the direction information of several electromagnetic waves/sources from the outputs of a number of receiving antennas that form a sensor array. DOA estimation is a major problem in array signal processing and has wide applications in radar, sonar, wireless communications, etc. With the development of sparse representation and compressed sensing, the last decade has witnessed a tremendous advance in this research topic. The purpose of this article is to provide an overview of these sparse methods for DOA estimation, with a particular highlight on the recently developed gridless sparse methods, e.g., those based on covariance fitting and the atomic norm. Several future research directions are also discussed.
Subfields: Computational Mechanics, Signal Processing
View Paper: https://www.semanticscholar.org/paper/2c5ece70052499d2bf6ca105799b56f5a6687cd8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Georgios K. Papageorgiou
- Mathini Sellathurai
- Yonina C. Eldar
- Marco F. Duarte
- Moshe Mishali
- Zhilin Zhang
- Richard Baraniuk
- Bhaskar D. Rao
In Signal Processing
- Yonina C. Eldar
- Georgios K. Papageorgiou
- Mathini Sellathurai
- Marco F. Duarte
- Moshe Mishali
- Zhilin Zhang
- Richard Baraniuk
- Noureddine El Karoui
Citations in the corpus
- A Survey of Sound Source Localization with Deep Learning Methods (2021, unknown): “We do not detail those papers as well, since we focus on SSL in real-world reverberant environments.”
- Deep Networks for Direction-of-Arrival Estimation in Low SNR (2020, methodology): “CS techniques are generally separated into three main categories: a) on-grid, b) off-grid and c) grid-less methods [11], [12].”