@inproceedings{a095f0c0350f47d9a235d1d87c416b3b,
title = "Atomic norm method for DOA estimation in random sampling condition",
abstract = "A novel direction-of-arrival (DOA) estimation method based on atomic norm in missing data condition is proposed. This method can exactly recover the missing data in random distribution and get the precise estimated result. A generalized atomic norm model with missing data is built to change the data recovery problem into a semidefinite programming issue. The dual problem and parameters selection are also analyzed to accelerate the proposed method. An enhanced covariance matrix sparse representation method is introduced for final DOA estimation. This method can deal with the coherent signal condition without any decorrelation preprocessing. Simulation experiments are conducted to validate the effectiveness of the proposed method and the results of several existing sparse representation based methods are also given for comparison.",
keywords = "Atomic norm, Data recovery, Direction-of-arrival (DOA)",
author = "Tong Qian and Jing Tian and Xu Zhang and Wei Cui",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 CIE International Conference on Radar, RADAR 2016 ; Conference date: 10-10-2016 Through 13-10-2016",
year = "2017",
month = oct,
day = "4",
doi = "10.1109/RADAR.2016.8059530",
language = "English",
series = "2016 CIE International Conference on Radar, RADAR 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 CIE International Conference on Radar, RADAR 2016",
address = "United States",
}