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An Application of the Orthogonal Matching Pursuit Algorithm in Space-Time Adaptive Processing

The article presents a new space-time adaptive processing (STAP) method for target detection in a heterogeneous and non-stationary environment. In study it was proven that it is possible to estimate the clutter covariance matrix (CCM) in STAP by using the MIMO (Multiple Input Multiple Output) radar...

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Detalles Bibliográficos
Autores principales: Ślesicka, Anna, Kawalec, Adam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349245/
https://www.ncbi.nlm.nih.gov/pubmed/32575499
http://dx.doi.org/10.3390/s20123468
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author Ślesicka, Anna
Kawalec, Adam
author_facet Ślesicka, Anna
Kawalec, Adam
author_sort Ślesicka, Anna
collection PubMed
description The article presents a new space-time adaptive processing (STAP) method for target detection in a heterogeneous and non-stationary environment. In study it was proven that it is possible to estimate the clutter covariance matrix (CCM) in STAP by using the MIMO (Multiple Input Multiple Output) radar geometry model and the orthogonal matching pursuit (OMP) algorithm. For the estimation of spatio-temporal spectrum of clutter and target, a model of joint sparse recovery was established. As a result, clutter suppression and target detection in a heterogeneous environment will be achieved. In addition, the proposed method uses a single snapshot of the radar data cube, which eliminates the need for access to all training cells.
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spelling pubmed-73492452020-07-22 An Application of the Orthogonal Matching Pursuit Algorithm in Space-Time Adaptive Processing Ślesicka, Anna Kawalec, Adam Sensors (Basel) Letter The article presents a new space-time adaptive processing (STAP) method for target detection in a heterogeneous and non-stationary environment. In study it was proven that it is possible to estimate the clutter covariance matrix (CCM) in STAP by using the MIMO (Multiple Input Multiple Output) radar geometry model and the orthogonal matching pursuit (OMP) algorithm. For the estimation of spatio-temporal spectrum of clutter and target, a model of joint sparse recovery was established. As a result, clutter suppression and target detection in a heterogeneous environment will be achieved. In addition, the proposed method uses a single snapshot of the radar data cube, which eliminates the need for access to all training cells. MDPI 2020-06-19 /pmc/articles/PMC7349245/ /pubmed/32575499 http://dx.doi.org/10.3390/s20123468 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Letter
Ślesicka, Anna
Kawalec, Adam
An Application of the Orthogonal Matching Pursuit Algorithm in Space-Time Adaptive Processing
title An Application of the Orthogonal Matching Pursuit Algorithm in Space-Time Adaptive Processing
title_full An Application of the Orthogonal Matching Pursuit Algorithm in Space-Time Adaptive Processing
title_fullStr An Application of the Orthogonal Matching Pursuit Algorithm in Space-Time Adaptive Processing
title_full_unstemmed An Application of the Orthogonal Matching Pursuit Algorithm in Space-Time Adaptive Processing
title_short An Application of the Orthogonal Matching Pursuit Algorithm in Space-Time Adaptive Processing
title_sort application of the orthogonal matching pursuit algorithm in space-time adaptive processing
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349245/
https://www.ncbi.nlm.nih.gov/pubmed/32575499
http://dx.doi.org/10.3390/s20123468
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