@inproceedings{202935e504df4e59b99ce2f8e347efb0,
title = "Sparsity-based frequency-hopping spectrum estimation with missing samples",
abstract = "In this paper, we address the problem of spectrum estimation of frequency-hopping (FH) signals in the presence of random missing samples. The signals are analyzed within the bilinear time-frequency representation framework, where a time-frequency kernel is designed based on inherent FH signal structures. The designed kernel permits effective suppression of cross-Terms and artifacts due to missing samples while preserving the FH signal auto-Terms. The kernelled results are represented in the instantaneous autocorrelation function domain, which are then processed using sparse reconstruction methods for high-resolution estimation of the FH signal time-frequency spectrum. The proposed method achieves accurate FH signal spectrum estimation even when a large proportion of data samples is missing. Simulation results verify the effectiveness of the proposed method and its superiority over existing techniques.",
keywords = "Frequency hopping, kernel design, missing samples, sparse reconstruction, spectrum estimation, time-frequency distribution",
author = "Shengheng Liu and Zhang, \{Yimin D.\} and Tao Shan",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Radar Conference, RadarConf 2016 ; Conference date: 02-05-2016 Through 06-05-2016",
year = "2016",
month = jun,
day = "3",
doi = "10.1109/RADAR.2016.7485265",
language = "English",
series = "2016 IEEE Radar Conference, RadarConf 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE Radar Conference, RadarConf 2016",
address = "United States",
}