tailieunhanh - báo cáo hóa học:" Research Article Robust Time-Frequency Distributions with Complex-Lag Argument"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Robust Time-Frequency Distributions with Complex-Lag Argument | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 879874 10 pages doi 2010 879874 Research Article Robust Time-Frequency Distributions with Complex-Lag Argument Nikola Zaric Irena Orovic and Srdjan Stankovic Faculty of Electrical Engineering University of Montenegro Podgorica 20000 Montenegro Correspondence should be addressed to Nikola Zaric zaric@ Received 30 December 2009 Accepted 1 March 2010 Academic Editor Igor Djurovic Copyright 2010 Nikola Zaric et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. The robust time-frequency distributions with complex-lag argument are proposed. They can provide an accurate estimation of fast varying instantaneous frequency in the presence of noise with heavy-tailed probability density function. The L-estimate form of this distribution is defined and it includes the L-estimate form of Wigner distribution as a special case. A modification for multicomponent signal representation is proposed as well. Theoretical considerations are illustrated by the examples. 1. Introduction Nonstationary signals such as speech radar seismic sonar and biomedical signals can be found in many practical applications. Due to time-varying spectra of these signals time-frequency analysis has been used in their analysis. For different types of signals various time-frequency distributions TFDs have been proposed 1-5 . In real applications we deal with signals corrupted by noise. If noise is additive with Gaussian probability density function pdf the standard time-frequency distributions represent a maximum likelihood ML estimate 6 . However if the signal is corrupted by noise with heavy-tailed pdf usually caused by environmental or human-made activities the standard TFDs produce poor results. Consequently the robust .