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Performance Bound for Extended Target Tracking Using High Resolution Sensors
This article concerns the problem of the estimation bound for tracking an extended target observed by a high resolution sensor. Two types of commonly used models for extended targets and the corresponding posterior Cramer-Rao lower bound (PCRLB) are discussed. The first type is the equation-extensio...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231100/ https://www.ncbi.nlm.nih.gov/pubmed/22163546 http://dx.doi.org/10.3390/s101211618 |
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author | Zhong, Zhiwen Meng, Huadong Zhang, Hao Wang, Xiqin |
author_facet | Zhong, Zhiwen Meng, Huadong Zhang, Hao Wang, Xiqin |
author_sort | Zhong, Zhiwen |
collection | PubMed |
description | This article concerns the problem of the estimation bound for tracking an extended target observed by a high resolution sensor. Two types of commonly used models for extended targets and the corresponding posterior Cramer-Rao lower bound (PCRLB) are discussed. The first type is the equation-extension model which extends the state space to include parameters such as target size and shape. Thus, the extended state vector can be estimated through the measurements obtained by a high resolution sensor. The measurement vector is also an expansion of the conventional one, and the additional measurements such as target extent can provide extra information for the estimation. The second model is based on multiple target measurements, each of which is an independent random draw from a spatial probability distribution. As the number of measurements per frame is unknown and random, the general form of the measurement contribution to the Fisher information matrix (FIM) conditional on the number of measurements is presented, and an extended information reduction factor (EIRF) approach is proposed to calculate the overall FIM and, therefore, the PCRLB. The bound of the second extended target model is also less than that of the point model, on condition that the average number of measurements is greater than one. Illustrative simulation examples of the two models are discussed and demonstrated. |
format | Online Article Text |
id | pubmed-3231100 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-32311002011-12-07 Performance Bound for Extended Target Tracking Using High Resolution Sensors Zhong, Zhiwen Meng, Huadong Zhang, Hao Wang, Xiqin Sensors (Basel) Article This article concerns the problem of the estimation bound for tracking an extended target observed by a high resolution sensor. Two types of commonly used models for extended targets and the corresponding posterior Cramer-Rao lower bound (PCRLB) are discussed. The first type is the equation-extension model which extends the state space to include parameters such as target size and shape. Thus, the extended state vector can be estimated through the measurements obtained by a high resolution sensor. The measurement vector is also an expansion of the conventional one, and the additional measurements such as target extent can provide extra information for the estimation. The second model is based on multiple target measurements, each of which is an independent random draw from a spatial probability distribution. As the number of measurements per frame is unknown and random, the general form of the measurement contribution to the Fisher information matrix (FIM) conditional on the number of measurements is presented, and an extended information reduction factor (EIRF) approach is proposed to calculate the overall FIM and, therefore, the PCRLB. The bound of the second extended target model is also less than that of the point model, on condition that the average number of measurements is greater than one. Illustrative simulation examples of the two models are discussed and demonstrated. Molecular Diversity Preservation International (MDPI) 2010-12-20 /pmc/articles/PMC3231100/ /pubmed/22163546 http://dx.doi.org/10.3390/s101211618 Text en © 2010 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/3.0/). |
spellingShingle | Article Zhong, Zhiwen Meng, Huadong Zhang, Hao Wang, Xiqin Performance Bound for Extended Target Tracking Using High Resolution Sensors |
title | Performance Bound for Extended Target Tracking Using High Resolution Sensors |
title_full | Performance Bound for Extended Target Tracking Using High Resolution Sensors |
title_fullStr | Performance Bound for Extended Target Tracking Using High Resolution Sensors |
title_full_unstemmed | Performance Bound for Extended Target Tracking Using High Resolution Sensors |
title_short | Performance Bound for Extended Target Tracking Using High Resolution Sensors |
title_sort | performance bound for extended target tracking using high resolution sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231100/ https://www.ncbi.nlm.nih.gov/pubmed/22163546 http://dx.doi.org/10.3390/s101211618 |
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