Kernel design for reduced interference distributions
IEEE Transactions on Signal Processing
Characterization of polynomial functions and application totime-frequency analysis
IEEE Transactions on Signal Processing
A signal-dependent time-frequency representation: optimal kerneldesign
IEEE Transactions on Signal Processing
Time-frequency distributions with complex argument
IEEE Transactions on Signal Processing
Polynomial Wigner-Ville distributions and their relationship totime-varying higher order spectra
IEEE Transactions on Signal Processing
A method for time-frequency analysis
IEEE Transactions on Signal Processing
Design of higher order polynomial Wigner-Ville distributions
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Kernel design for time-frequency signal analysis using the Radontransform
IEEE Transactions on Signal Processing
Multicomponent AM–FM Representations: An Asymptotically Exact Approach
IEEE Transactions on Audio, Speech, and Language Processing
Hi-index | 0.08 |
The conventional time-frequency distributions for multicomponent polynomial phase signals (PPS) generally suffer from interference terms, which will obscure the true location of the auto-components in the resulting time-frequency distributions. In this paper, three schemes for designing the generalized time-frequency distributions for multicomponent PPS based on the matched-phase principle are presented, and they are illustrated as follows:(1)the scheme based on the Wigner-Ville distribution (WVD) and the L-Wigner-Ville distribution (LWVD); (2)the scheme based on the fractional matched-phase principle; (3)the scheme based on the complex lags. The interference terms induced by the nonlinearity of the signals can be suppressed; for multicomponent signals, the CLEAN technique is adopted to filter out each component by a band-pass filter, and the interference terms between different components can be suppressed (eliminated). The new generalized time-frequency distributions are superimposed to yield a high-readability representation.