@inproceedings{80e767e6528a4d27b2d23021a2d95b4d,
title = "Direction finding using sparse linear arrays with missing data",
abstract = "We investigate the problem of direction of arrival (DOA) estimation using sparse linear arrays, such as co-prime and nested arrays, in the case of missing data resulting from sensor failures. We introduce a signal model where sensor failures occur after taking certain number of snapshots. We formulate a structured covariance estimation problem by exploiting the special geometry of sparse linear arrays, which also provides enhanced degrees of freedom. Numerical examples show that, by utilizing the information in both complete measurements and incomplete measurements, our method achieves better estimation accuracy than the traditional method using only complete measurements.",
keywords = "coprime array, DOA estimation, maximum likelihood, missing data, nested array",
author = "Mianzhi Wang and Zhen Zhang and Arye Nehorai",
note = "Funding Information: This work was supported by the ONR Grant N000141310050. Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952720",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3066--3070",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}