IEEE Transactions on Signal Processing
Techniques to obtain good resolution and concentrated time-frequency distributions: a review
EURASIP Journal on Advances in Signal Processing
EURASIP Journal on Advances in Signal Processing
Vector time-frequency AR models for nonstationary multivariate random processes
IEEE Transactions on Signal Processing
Efficient algorithms for adaptive capon and APES spectral estimation
IEEE Transactions on Signal Processing
Estimation of ambiguity functions with limited spread
IEEE Transactions on Signal Processing
EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
High-resolution time-frequency methods performance analysis
EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
Approximating the time-frequency representation of biosignals with chirplets
EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
Maximally flat differentiators through WLS Taylor decomposition
Digital Signal Processing
LFM signal detection using LPP-Hough transform
Signal Processing
Validity-guided fuzzy clustering evaluation for neural network-based time-frequency reassignment
EURASIP Journal on Advances in Signal Processing - Special issue on time-frequency analysis and its applications to multimedia signals
Hi-index | 35.70 |
Parsimonious parametric models for nonstationary random processes are useful in many applications. Here, we consider a nonstationary extension of the classical autoregressive moving-average (ARMA) model that we term the time-frequency autoregressive moving-average (TFARMA) model. This model uses frequency shifts in addition to time shifts (delays) for modeling nonstationary process dynamics. The TFARMA model and its special cases, the TFAR and TFMA models, are shown to be specific types of time-varying ARMA (AR, MA) models. They are attractive because of their parsimony for underspread processes, that is, nonstationary processes with a limited time-frequency correlation structure. We develop computationally efficient order-recursive estimators for the TFARMA, TFAR, and TFMA model parameters which are based on linear time-frequency Yule-Walker equations or on a new time-frequency cepstrum. Simulation results demonstrate that the proposed parameter estimators outperform existing estimators for time-varying ARMA (AR, MA) models with respect to accuracy and/or numerical efficiency. An application to the time-varying spectral analysis of a natural signal is also discussed.