Direction finding based on spatial time-frequency distribution matrices.

Spatial time-frequency distributions (STFDs) have been recently introduced as the natural means to deal with source signals that are localizable in the time-frequency domain. It has been shown that improved estimates of the signal and noise subspaces are achieved by constructing the subspaces from the time-frequency signatures of the signal arrivals rather than from the data covariance matrices, which are commonly used in conventional subspace estimation methods. This paper discusses the application of STFD to high-resolution direction finding. We focus on both the role and the effect of crossterms in angle estimation when multiple timefrequency points are incorporated. Simulation examples are presented to compare the performance of joint block-diagonalization and time-frequency averaging techniques for incorporating multiple autoterm and crossterm points in subspace estimation.

Main Author: Amin, Moeness G.
Other Authors: Zhang, Yimin.
Format: Villanova Faculty Authorship
Language: English
Published: 2000
Online Access: http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:173258
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author Amin, Moeness G.
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Zhang, Yimin.
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Zhang, Yimin.
author_s Amin, Moeness G.
spellingShingle Amin, Moeness G.
Direction finding based on spatial time-frequency distribution matrices.
author-letter Amin, Moeness G.
author_sort_str Amin, Moeness G.
author2 Zhang, Yimin.
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dc_title_str Direction finding based on spatial time-frequency distribution matrices.
title Direction finding based on spatial time-frequency distribution matrices.
title_short Direction finding based on spatial time-frequency distribution matrices.
title_full Direction finding based on spatial time-frequency distribution matrices.
title_fullStr Direction finding based on spatial time-frequency distribution matrices.
title_full_unstemmed Direction finding based on spatial time-frequency distribution matrices.
collection_title_sort_str direction finding based on spatial time-frequency distribution matrices.
title_sort direction finding based on spatial time-frequency distribution matrices.
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description Spatial time-frequency distributions (STFDs) have been recently introduced as the natural means to deal with source signals that are localizable in the time-frequency domain. It has been shown that improved estimates of the signal and noise subspaces are achieved by constructing the subspaces from the time-frequency signatures of the signal arrivals rather than from the data covariance matrices, which are commonly used in conventional subspace estimation methods. This paper discusses the application of STFD to high-resolution direction finding. We focus on both the role and the effect of crossterms in angle estimation when multiple timefrequency points are incorporated. Simulation examples are presented to compare the performance of joint block-diagonalization and time-frequency averaging techniques for incorporating multiple autoterm and crossterm points in subspace estimation.
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dc.title Direction finding based on spatial time-frequency distribution matrices.
dc.creator Amin, Moeness G.
Zhang, Yimin.
dc.description Spatial time-frequency distributions (STFDs) have been recently introduced as the natural means to deal with source signals that are localizable in the time-frequency domain. It has been shown that improved estimates of the signal and noise subspaces are achieved by constructing the subspaces from the time-frequency signatures of the signal arrivals rather than from the data covariance matrices, which are commonly used in conventional subspace estimation methods. This paper discusses the application of STFD to high-resolution direction finding. We focus on both the role and the effect of crossterms in angle estimation when multiple timefrequency points are incorporated. Simulation examples are presented to compare the performance of joint block-diagonalization and time-frequency averaging techniques for incorporating multiple autoterm and crossterm points in subspace estimation.
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