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Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars

The problem of obtaining high range resolution (HRR) profiles for non-cooperative target recognition by coherently combining data from narrowband radars was investigated using sparse reconstruction techniques. If the radars concerned operate within different frequency bands, then this process increa...

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Detalles Bibliográficos
Autores principales: Berry, Paul, Nguyen, Ngoc Hung, Tran, Hai-Tan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038352/
https://www.ncbi.nlm.nih.gov/pubmed/31991710
http://dx.doi.org/10.3390/s20030665
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author Berry, Paul
Nguyen, Ngoc Hung
Tran, Hai-Tan
author_facet Berry, Paul
Nguyen, Ngoc Hung
Tran, Hai-Tan
author_sort Berry, Paul
collection PubMed
description The problem of obtaining high range resolution (HRR) profiles for non-cooperative target recognition by coherently combining data from narrowband radars was investigated using sparse reconstruction techniques. If the radars concerned operate within different frequency bands, then this process increases the overall effective bandwidth and consequently enhances resolution. The case of unknown range offsets occurring between the radars’ range profiles due to incorrect temporal and spatial synchronisation between the radars was considered, and the use of both pruned orthogonal matching pursuit and refined [Formula: see text]-norm regularisation solvers was explored to estimate the offsets between the radars’ channels so as to attain the necessary coherence for combining their data. The proposed techniques were demonstrated and compared using simulated radar data.
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spelling pubmed-70383522020-03-09 Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars Berry, Paul Nguyen, Ngoc Hung Tran, Hai-Tan Sensors (Basel) Article The problem of obtaining high range resolution (HRR) profiles for non-cooperative target recognition by coherently combining data from narrowband radars was investigated using sparse reconstruction techniques. If the radars concerned operate within different frequency bands, then this process increases the overall effective bandwidth and consequently enhances resolution. The case of unknown range offsets occurring between the radars’ range profiles due to incorrect temporal and spatial synchronisation between the radars was considered, and the use of both pruned orthogonal matching pursuit and refined [Formula: see text]-norm regularisation solvers was explored to estimate the offsets between the radars’ channels so as to attain the necessary coherence for combining their data. The proposed techniques were demonstrated and compared using simulated radar data. MDPI 2020-01-24 /pmc/articles/PMC7038352/ /pubmed/31991710 http://dx.doi.org/10.3390/s20030665 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 Article
Berry, Paul
Nguyen, Ngoc Hung
Tran, Hai-Tan
Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars
title Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars
title_full Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars
title_fullStr Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars
title_full_unstemmed Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars
title_short Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars
title_sort compressive sensing-based bandwidth stitching for multichannel microwave radars
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038352/
https://www.ncbi.nlm.nih.gov/pubmed/31991710
http://dx.doi.org/10.3390/s20030665
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