@inproceedings{ca7bdc0c6f364db5a020c45d5b2809d8,
title = "Fast covariance matrix sparse representation for DOA estimation based on dynamic dictionary",
abstract = "This paper proposes a fast covariance matrix sparse representation method for direction of arrival estimation based on dynamic dictionary as a solution to the off-grid effect. The statistic information of covariance matrix under the uncorrelated sources condition is utilized and a simple sparse representation model is given. Then the cross iteration and series expansion approximation are introduced to update the dynamic dictionary. The parameters selection is also discussed in the paper. The simulation results show that the proposed method can efficiently reduce the off-grid effect and the over-complete rate of the original dictionary. Compared with the conventional sparse representation method, it has better performance and lower computation complexity.",
keywords = "direction of arrival (DOA), dynamic dictionary, sparse representation",
author = "Tong Qian and Xiang, {Jin Zhi} and Wei Cui",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 13th IEEE International Conference on Signal Processing, ICSP 2016 ; Conference date: 06-11-2016 Through 10-11-2016",
year = "2016",
month = jul,
day = "2",
doi = "10.1109/ICSP.2016.7877812",
language = "English",
series = "International Conference on Signal Processing Proceedings, ICSP",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "138--143",
editor = "Yuan Baozong and Ruan Qiuqi and Zhao Yao and An Gaoyun",
booktitle = "ICSP 2016 - 2016 IEEE 13th International Conference on Signal Processing, Proceedings",
address = "United States",
}