Luke A. Cirillo

Parameter Estimation for Locally Linear FM Signals Using a Time-Frequency Hough Transform

An estimator for the phase parameters of mono- and multicomponent FM signals, with both good numerical properties and statistical performance is proposed. The proposed approach is based on the Hough transform of the pseudo-Wigner-Ville time-frequency distribution (PWVD). It is shown that the numerical properties of the estimator can be improved by varying the PWVD window length. The effect of the window time extent on the statistical performance of the estimator is delineated. Experimental data is used for validation of the statistical properties.

Morphological image processing for FM source detection and localization

We consider the problem of direction finding for frequency modulated signals impinging on an array of sensors. Making use of a time-frequency representation of the data, we are able to exploit the non-stationary nature of the source signals. We employ morphological image processing to estimate time-frequency signature segments of each source. For the direction finding we apply beamforming techniques on averaged spatial time-frequency distribution matrices. When they occur, overlapping segments are splitted and re-combined after direction finding. The re-combination of segments is based on a bootstrap test, which resamples time-frequency auto-term locations. The proposed method also allows direction finding for the underdetermined case, i.e. when there are more sources than the number of array sensors. Furthermore, it detects the number of sources present, and the detector performance is compared to information based criteria.

Automatic classification of auto-and cross-terms of time-frequency distributions in antenna arrays

The problem of selecting auto- and cross-terms of time-frequency distributions (TFDs) of nonstationary signals impinging on a multi-antenna receiver is considered. A detection approach is introduced which allows performance measurement and comparison of various schemes via receiver operating characteristics. Array averaging and array differencing techniques are both employed to form a basis for time-frequency (t-f) point selection. The proposed classification method is evaluated against the bootsrap-based method. It is shown that the former offers improved performance and simplified implementations.

Estimation of Fm Parameters Using a Time-Frequency Hough Transform

An estimator for the phase parameters of mono- and multi-component FM signals, with both good numerical properties and statistical performance is proposed. The proposed approach is based on the Hough transform of the pseudo Wigner-Ville time-frequency distribution (PWVD). It is shown that the numerical properties of the estimator may be improved by varying the PWVD window length. The effect of the window time extent on the statistical performance of the estimator is delineated. Experimental data is used for validation of statistical properties

Direction finding of nonstationary signals using a time-frequency Hough transform

We consider the problem of direction finding for nonstationary signals impinging on an array of sensors. Making use of a time-frequency representation of the data, we are able to exploit the non-stationary nature of the source signals. We employ a generalized Hough transform to estimate the time-frequency signature of each source. The proposed method also allows direction finding when there are more sources than the number of array sensors.

Auto-term detection using time-frequency array processing

The problem of nonstationary signal detection using antenna arrays is considered. A method for detecting source signal auto-term regions in the time-frequency plane is presented, based on spatial time-frequency distribution (STFD) matrices. A general signal detection framework when using arbitrary time-frequency kernels is proposed and the trace of a whitened STFD matrix is used to form an appropriate test statistic. The expressions for the mean and variance of the statistic, necessary to evaluate the test, are provided. The detector performance using the Wigner-Ville distribution is investigated via simulated and theoretical results.

Direction Finding of Nonstationary Signals using Spatial Time-Frequency Distributions and Morphological Image Processing

We consider the problem of direction finding for nonstationary signals impinging on an array of sensors. Making use of a time-frequency representation of the data, we are able to exploit the non-stationary nature of the source signals. We employ morphological image processing to estimate time-frequency signature segments of each source. Optional overlapping segments are splitted and recombined after direction finding. The proposed method also allows direction finding for the underdetermined case, i.e. when there are more sources than the number of array sensors.

Estimation of Near-Field Parameters using Spatial Time-Frequency Distributions

This work deals with the estimation of near-field parameters using passive sensor arrays. A transformation of the array data is proposed which allows the extraction of near-field time-frequency signatures from data containing a mixture of far- and near-field sources. Spatial time-frequency distribution matrices are then used as a means for solving the near-field parameter estimation problem. The estimation accuracy of the proposed approach is compared to existing methods via simulation analysis. An experimental validation of theoretical ideas is also presented.

Direction-of-arrival estimation for uncorrelated FM signals

A method of direction finding for FM signals based on averaged spatial time-frequency distributions is presented. This technique has been applied previously to algorithms such as MUSIC and maximum likelihood (ML). However, these estimation techniques do not take full advantage of the underlying diagonal structure of the source matrices. It is demonstrated that, by using the direction estimation for uncorrelated emitters (DEUCE) algorithm (Jansson, M. et al., IEEE Trans. Sig. Processing, vol.47, no.4, p.945-56, 1999), we can improve the performance in estimating the source directions. To use this approach, knowledge of the source auto-term locations on the time-frequency plane is required. In the absence of prior knowledge about the time-frequency signatures of the sources, an automatic point selection procedure is incorporated. The proposed scheme is demonstrated to yield results close to the optimal case of known auto-term locations.

On blind separation of nonstationary signals

In this paper we consider a time-frequency based approach to blind separation of nonstationary signals. In particular, we propose a time-frequency ‘point selection’ algorithm based on multiple hypothesis testing, which allows automatic selection of auto- or cross-source locations on the time-frequency plane. The chosen points are used via a joint diagonalization and off-diagonalization algorithm to perform source separation. A performance comparison of the proposed and existing approaches is provided.