Results 201 to 210 of about 1,527,120 (243)

Robust time-frequency distributions

Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467), 2002
The time-frequency (TF) distributions robust with respect to the heavy-tailed impulse noise are introduced. The robust spectrogram (SPEC) and robust Wigner distribution (WD) are considered. The calculation procedure and properties of these representations are given.
Vladimir Katkovnik, Igor Djurovic, Ljubisa Stankovic   +2 more
openaire   +1 more source

Time-frequency distribution series

Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing, 2002
Using the orthogonal-like Gabor (1946) expansion, we decompose the Wigner-Ville (1932, 1948) distribution as a linear combination of localized and oscillated 2D Gaussian functions. Based on the degree of oscillation, we further group those 2D Gaussian functions into time-frequency functions P/sub d/(t,/spl omega/) and thereby obtain the so-called time ...
Shie Qian, Dapang Chen
openaire   +1 more source

Spatial time-frequency distributions and their applications

Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467), 2002
The evaluation of quadratic time-frequency distributions (TFDs) of nonstationary signals impinging on a multisensor receiver yields spatial time-frequency distributions (STFDs). We review the concept of STFDs and their applications to blind source separation and direction finding problems.
Moeness G. Amin, Yimin Zhang 0001
openaire   +1 more source

On generalized-marginal time-frequency distributions

IEEE Transactions on Signal Processing, 1996
We introduce a family of time-frequency (TF) distributions with generalized marginals, i.e., beyond the time-domain and the frequency-domain marginals, in the sense that the projections of a TF distribution along one or more angles are equal to the magnitude squared of the fractional Fourier transforms of the signal.
Xiang-Gen Xia 0001, Yuri Owechko, Bernard H. Soffer, Roy M. Matic   +3 more
openaire   +1 more source

Positive time-frequency distribution functions

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985
We demonstrate the existence of positive joint distributions of time and frequency for arbitrary signals. A method is given to readily generate an infinite number of them for any signal. General properties of these distribution functions are derived and specific examples for some common signals are presented.
Leon Cohen, Theodore E. Posch
openaire   +1 more source

Spatial averaging of time-frequency distributions

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999
This paper presents a novel approach based on time-frequency distributions (TFDs) for separating signals received by a multiple antenna array. This approach provides a significant improvement in performance over the previously introduced spatial time-frequency distributions, specifically for signals with close time-frequency signatures.
Yimin Zhang 0001, Moeness G. Amin
openaire   +1 more source

Minimum entropy time-frequency distributions

IEEE Signal Processing Letters, 2005
Re/spl acute/nyi entropy has been proposed as an effective measure of signal information content and complexity on the time-frequency plane. The previous work concerning Re/spl acute/nyi entropy in the time-frequency plane has focused on measuring the complexity of a given deterministic signal.
Selin Aviyente, William J. Williams
openaire   +1 more source

Time-frequency distributions-a review

Proceedings of the IEEE, 1989
A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented. The objective of the field is to describe how the spectral content of a signal changes in time and to develop the physical and mathematical ideas needed to understand what a time-varying spectrum is.
openaire   +1 more source

Multitaper marginal time–frequency distributions

Signal Processing, 2006
Time-frequency distributions (TFDs) belonging to Cohen's class yield a frequency marginal that is equivalent to the periodogram of the signal. It is well-known that the periodogram is not a good spectral estimator since it is not a consistent estimate, i.e. its variance does not decrease with the sample size. Thomson addressed this issue by introducing
Selin Aviyente, William J. Williams
openaire   +1 more source