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Statistical Analysis of the Performance of MDL Enumeration for Multiple-Missed Detection in Array Processing

An accurate performance analysis on the MDL criterion for source enumeration in array processing is presented in this paper. The enumeration results of MDL can be predicted precisely by the proposed procedure via the statistical analysis of the sample eigenvalues, whose distributive properties are i...

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Detalles Bibliográficos
Autores principales: Du, Fei, Li, Yibo, Jin, Shijiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4570420/
https://www.ncbi.nlm.nih.gov/pubmed/26295232
http://dx.doi.org/10.3390/s150820250
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author Du, Fei
Li, Yibo
Jin, Shijiu
author_facet Du, Fei
Li, Yibo
Jin, Shijiu
author_sort Du, Fei
collection PubMed
description An accurate performance analysis on the MDL criterion for source enumeration in array processing is presented in this paper. The enumeration results of MDL can be predicted precisely by the proposed procedure via the statistical analysis of the sample eigenvalues, whose distributive properties are investigated with the consideration of their interactions. A novel approach is also developed for the performance evaluation when the source number is underestimated by a number greater than one, which is denoted as “multiple-missed detection”, and the probability of a specific underestimated source number can be estimated by ratio distribution analysis. Simulation results are included to demonstrate the superiority of the presented method over available results and confirm the ability of the proposed approach to perform multiple-missed detection analysis.
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spelling pubmed-45704202015-09-17 Statistical Analysis of the Performance of MDL Enumeration for Multiple-Missed Detection in Array Processing Du, Fei Li, Yibo Jin, Shijiu Sensors (Basel) Article An accurate performance analysis on the MDL criterion for source enumeration in array processing is presented in this paper. The enumeration results of MDL can be predicted precisely by the proposed procedure via the statistical analysis of the sample eigenvalues, whose distributive properties are investigated with the consideration of their interactions. A novel approach is also developed for the performance evaluation when the source number is underestimated by a number greater than one, which is denoted as “multiple-missed detection”, and the probability of a specific underestimated source number can be estimated by ratio distribution analysis. Simulation results are included to demonstrate the superiority of the presented method over available results and confirm the ability of the proposed approach to perform multiple-missed detection analysis. MDPI 2015-08-18 /pmc/articles/PMC4570420/ /pubmed/26295232 http://dx.doi.org/10.3390/s150820250 Text en © 2015 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 license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Du, Fei
Li, Yibo
Jin, Shijiu
Statistical Analysis of the Performance of MDL Enumeration for Multiple-Missed Detection in Array Processing
title Statistical Analysis of the Performance of MDL Enumeration for Multiple-Missed Detection in Array Processing
title_full Statistical Analysis of the Performance of MDL Enumeration for Multiple-Missed Detection in Array Processing
title_fullStr Statistical Analysis of the Performance of MDL Enumeration for Multiple-Missed Detection in Array Processing
title_full_unstemmed Statistical Analysis of the Performance of MDL Enumeration for Multiple-Missed Detection in Array Processing
title_short Statistical Analysis of the Performance of MDL Enumeration for Multiple-Missed Detection in Array Processing
title_sort statistical analysis of the performance of mdl enumeration for multiple-missed detection in array processing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4570420/
https://www.ncbi.nlm.nih.gov/pubmed/26295232
http://dx.doi.org/10.3390/s150820250
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