Multitaper marginal time-frequency distributions
Signal Processing
Extraction and Analysis of Multiple Periodic Motions in Video Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Signal Processing
The transient spectrum of a random system
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 recent advances in theory and methods for nonstationary signal analysis
Anechoic Blind Source Separation Using Wigner Marginals
The Journal of Machine Learning Research
Hi-index | 35.69 |
Current theories of a time-varying spectrum of a nonstationary process all involve, either by definition or by difficulties in estimation, an assumption that the signal statistics vary slowly over time. This restrictive quasistationarity assumption limits the use of existing estimation techniques to a small class of nonstationary processes. We overcome this limitation by deriving a statistically optimal kernel, within Cohen's (1989) class of time-frequency representations (TFR's), for estimating the Wigner-Ville spectrum of a nonstationary process. We also solve the related problem of minimum mean-squared error estimation of an arbitrary bilinear TFR of a realization of a process from a correlated observation. Both optimal time-frequency invariant and time-frequency varying kernels are derived. It is shown that in the presence of any additive independent noise, optimal performance requires a nontrivial kernel and that optimal estimation may require smoothing filters that are very different from those based on a quasistationarity assumption. Examples confirm that the optimal estimators often yield tremendous improvements in performance over existing methods. In particular, the ability of the optimal kernel to suppress interference is quite remarkable, thus making the proposed framework potentially useful for interference suppression via time-frequency filtering