We study the instantaneous frequency IF of continuoustime, complex-valued, zero-mean, proper, mean-square differentiable, nonstationary Gaussian stochastic processes. We compute the probability density function for the IF for fixed time, which generalizes a result known for wide-sense stationary processes to nonstationary processes. For a fixed point in time, the IF has either zero or infinite variance. For harmonizable processes, we obtain as a consequence the result that the mean of the IF, for fixed time, is the normalized first-order frequency moment of the Wigner spectrum.We study the instantaneous frequency IF of continuoustime, complex-valued, zero-mean, proper, mean-square differentiable, nonstationary Gaussian stochastic pr...
International audienceStochastic integration with respect to Gaussian processes has raised strong in...
Spectral analysis of time varying signals is traditionally performed with the short time Fourier tra...
Harmonizable processes form a huge and useful class of non-stationary random processes. In this pape...
In this correspondence, the form of the one-dimensional probability distribution function for the Wi...
Most of the stochastic processes used to model physical processes are nonstationary, and yet most of...
This paper treats estimation of the Wigner-Ville spectrum (WVS) of Gaussian continuous-time stochast...
We have previously presented a method to write equations for the Wigner distribution corresponding t...
The paper treats the Wigner distribution of scalar-valued stochastic processes defined on R-d. We sh...
Stochastic calculus methods are used to estimate the instantaneous frequency of a signal. The frequ...
AbstractLet X and Y be random vectors of the same dimension such that Y has a normal distribution wi...
In this paper, the problem of choosing a method for time-frequency signal analysis is discussed. It ...
Abstract.We study estimation of theWigner time-frequency spectrum of Gaussian stochastic processes. ...
The aim of this work is to define and perform a study of local times of all Gaussian processes that ...
The detection of events in a stochastic signal has been a subject of great interest. One of the olde...
concentrated time–frequency (TF) representation of nonsta-tionary signals. It may be used as an effi...
International audienceStochastic integration with respect to Gaussian processes has raised strong in...
Spectral analysis of time varying signals is traditionally performed with the short time Fourier tra...
Harmonizable processes form a huge and useful class of non-stationary random processes. In this pape...
In this correspondence, the form of the one-dimensional probability distribution function for the Wi...
Most of the stochastic processes used to model physical processes are nonstationary, and yet most of...
This paper treats estimation of the Wigner-Ville spectrum (WVS) of Gaussian continuous-time stochast...
We have previously presented a method to write equations for the Wigner distribution corresponding t...
The paper treats the Wigner distribution of scalar-valued stochastic processes defined on R-d. We sh...
Stochastic calculus methods are used to estimate the instantaneous frequency of a signal. The frequ...
AbstractLet X and Y be random vectors of the same dimension such that Y has a normal distribution wi...
In this paper, the problem of choosing a method for time-frequency signal analysis is discussed. It ...
Abstract.We study estimation of theWigner time-frequency spectrum of Gaussian stochastic processes. ...
The aim of this work is to define and perform a study of local times of all Gaussian processes that ...
The detection of events in a stochastic signal has been a subject of great interest. One of the olde...
concentrated time–frequency (TF) representation of nonsta-tionary signals. It may be used as an effi...
International audienceStochastic integration with respect to Gaussian processes has raised strong in...
Spectral analysis of time varying signals is traditionally performed with the short time Fourier tra...
Harmonizable processes form a huge and useful class of non-stationary random processes. In this pape...