The problem of estimating the distribution of energy arriving at an array as a function of bearing is central to many array processing applications. To form such spatial spectral estimates, data is collected over an interval of time. If the geometry between the source and receiver varies over that interval then the spatial spectral estimator will lose resolution. This work describes a methodology for mitigating this loss based on employing higher order correlation matrices that are robust to such non-stationarities
Smoothed nonparametric kernel spectral density estimates are considered for stationary data observed...
In recent research on AA theory one of the major issues is elaboration of methods for estimation of ...
We use moments from the covariance matrix for spatial panel data to estimate the param-eters of the ...
A novel approach to the problem of estimating a spatial spectrum in a non-stationary environment has...
The problem of computing the time-bearing representation for data from a sensor array is considered....
The problem of computing the time-bearing representation for data from a sensor array is considered....
Array processing for spatial spectrum estimation is reexamined from the vector space viewpoint with ...
Array processing algorithms generally assume that the received signal, composed of both narrowband s...
We present an approach for the spectral analysis of of non-stationary spectral processes, that is ba...
<p>Estimation of a time-varying field is essential for situational awareness in many subject areas. ...
Nonlinear methods of spectral analysis have become widely used for improving the arrival-angle resol...
International audienceSpectral estimation generally aims at determining from a single realization of...
International audienceSpectral estimation generally aims at determining from a single realization of...
Many factors existing in practical applications may limit the performance potential of a superresolu...
Smoothed nonparametric kernel spectral density estimates are considered for stationary data observed...
Smoothed nonparametric kernel spectral density estimates are considered for stationary data observed...
In recent research on AA theory one of the major issues is elaboration of methods for estimation of ...
We use moments from the covariance matrix for spatial panel data to estimate the param-eters of the ...
A novel approach to the problem of estimating a spatial spectrum in a non-stationary environment has...
The problem of computing the time-bearing representation for data from a sensor array is considered....
The problem of computing the time-bearing representation for data from a sensor array is considered....
Array processing for spatial spectrum estimation is reexamined from the vector space viewpoint with ...
Array processing algorithms generally assume that the received signal, composed of both narrowband s...
We present an approach for the spectral analysis of of non-stationary spectral processes, that is ba...
<p>Estimation of a time-varying field is essential for situational awareness in many subject areas. ...
Nonlinear methods of spectral analysis have become widely used for improving the arrival-angle resol...
International audienceSpectral estimation generally aims at determining from a single realization of...
International audienceSpectral estimation generally aims at determining from a single realization of...
Many factors existing in practical applications may limit the performance potential of a superresolu...
Smoothed nonparametric kernel spectral density estimates are considered for stationary data observed...
Smoothed nonparametric kernel spectral density estimates are considered for stationary data observed...
In recent research on AA theory one of the major issues is elaboration of methods for estimation of ...
We use moments from the covariance matrix for spatial panel data to estimate the param-eters of the ...