@inproceedings{7272fc756e86415492d37dcada87c4e1,
title = "Generalized Coherence Based Cyclic Frequency Estimation",
abstract = "The use of generalized coherence to measure correlation among multiple subspaces has been studied as an approach for detection of cyclostationarity in a recent series of papers by the authors. The detectors obtained were demonstrated in simulations to offer superior performance to other established cyclostationarity detectors in application scenarios representing cognitive radio multi-channel passive surveillance. This paper describes an approach to cyclic frequency estimation based on similar generalized coherence ideas. The performance of such an estimator is demonstrated and compared against three prominent cyclic frequency estimators in multiple simulation experiments involving signals with different modulation types.",
keywords = "Cyclic frequency, cyclic correlation, cyclostationarity, generalized coherence, spectral correlation",
author = "Songsri Sirianunpiboon and Howard, {Stephen D.} and Douglas Cochran",
note = "Funding Information: ACKNOWLEDGEMENT This work was supported in part by the U.S. Air Force under Grant No. FA9550-18-1-0190. Publisher Copyright: {\textcopyright} 2022 IEEE.; 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 ; Conference date: 31-10-2022 Through 02-11-2022",
year = "2022",
doi = "10.1109/IEEECONF56349.2022.10052022",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "281--285",
editor = "Matthews, {Michael B.}",
booktitle = "56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022",
}