TY - GEN
T1 - Robust adaptive Lomb periodogram for time-frequency analysis of signals with sinusoidal and transient components
AU - Zhang, Zhiguo
AU - Chan, Shing Chow
PY - 2005
Y1 - 2005
N2 - This article introduces a robust adaptive Lomb periodogram (RALP) for time-frequency (TF) analysis of time series with sinusoidal and transient components, which are possibly non-uniformly sampled. It extends the conventional Lomb spectrum by windowing the observation data and adaptively selects the window lengths by the intersection of confidence intervals (ICI) rule. The influence of transient components to conventional time-frequency representation can be moderated using M-estimation of robust statistics. Instead of treating the transient components as impulsive noise and removing them, the proposed RALP TF distribution yields separately a time domain representation of the transient components and a conventional TF representation of the sinusoidal components, which greatly improves the visualization and detection of these components. Simulation results show that the proposed RALP differentiates the two kinds of components well, and offers better time and frequency resolutions than the conventional Lomb periodogram.
AB - This article introduces a robust adaptive Lomb periodogram (RALP) for time-frequency (TF) analysis of time series with sinusoidal and transient components, which are possibly non-uniformly sampled. It extends the conventional Lomb spectrum by windowing the observation data and adaptively selects the window lengths by the intersection of confidence intervals (ICI) rule. The influence of transient components to conventional time-frequency representation can be moderated using M-estimation of robust statistics. Instead of treating the transient components as impulsive noise and removing them, the proposed RALP TF distribution yields separately a time domain representation of the transient components and a conventional TF representation of the sinusoidal components, which greatly improves the visualization and detection of these components. Simulation results show that the proposed RALP differentiates the two kinds of components well, and offers better time and frequency resolutions than the conventional Lomb periodogram.
UR - https://www.scopus.com/pages/publications/33646799842
U2 - 10.1109/ICASSP.2005.1416053
DO - 10.1109/ICASSP.2005.1416053
M3 - 会议稿件
AN - SCOPUS:33646799842
SN - 0780388747
SN - 9780780388741
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 493
EP - 496
BT - 2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Signal Proces. Education, Spec. Sessions
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Y2 - 18 March 2005 through 23 March 2005
ER -