TY - GEN
T1 - The Geometry of Coherence and Its Application to Cyclostationary Time Series
AU - Howard, Stephen D.
AU - Sirianunpiboon, Songsri
AU - Cochran, Douglas
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2018/8/29
Y1 - 2018/8/29
N2 - The consequences of cyclostationary structure in a random process have traditionally been described in terms of the correlation or coherence of pairs of particular time and frequency shifted versions of the process. However, cyclostationarity, and more generally almost cyclostationarity, are manifest in the mutual coherence of subspaces spanned by sets of time and frequency shifted versions of the process. The generalized coherence framework allows any finite collection of pertinent samples of the cyclic autocorrelation function estimates formed from the measured signal data to be combined into a detection statistic. This paper develops the subspace coherence theory of almost cyclostationary processes as a guide to constructing such detectors in both the time and spectral domains.
AB - The consequences of cyclostationary structure in a random process have traditionally been described in terms of the correlation or coherence of pairs of particular time and frequency shifted versions of the process. However, cyclostationarity, and more generally almost cyclostationarity, are manifest in the mutual coherence of subspaces spanned by sets of time and frequency shifted versions of the process. The generalized coherence framework allows any finite collection of pertinent samples of the cyclic autocorrelation function estimates formed from the measured signal data to be combined into a detection statistic. This paper develops the subspace coherence theory of almost cyclostationary processes as a guide to constructing such detectors in both the time and spectral domains.
KW - Coherence
KW - Cyclostationarity
KW - Multiple-channel detection
UR - http://www.scopus.com/inward/record.url?scp=85053838465&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053838465&partnerID=8YFLogxK
U2 - 10.1109/SSP.2018.8450812
DO - 10.1109/SSP.2018.8450812
M3 - Conference contribution
SN - 9781538615706
T3 - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018
SP - 766
EP - 770
BT - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE Statistical Signal Processing Workshop, SSP 2018
Y2 - 10 June 2018 through 13 June 2018
ER -