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A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar
Dissertation (MEng)--University of Pretoria, 2013.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2014
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| _version_ | 1867613589281439744 |
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| access_status_str | Open Access |
| author2 | De Villiers, Johan Pieter |
| author_browse | De Villiers, Johan Pieter |
| author_facet | De Villiers, Johan Pieter |
| collection | Thesis |
| dc_rights_str_mv | © 2013 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng)--University of Pretoria, 2013. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/33372 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:33.011Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/33372 A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar De Villiers, Johan Pieter allandefreitas1@gmail.com Nel, W.A.J. De Freitas, Allan Extended target Tracking High range-resolution radar High rangeresolution profile Particle filtering ISAR Particle Markov chain Monte-Carlo Particle marginal Metropolis-Hastings sampler Static parameter estimation UCTD Dissertation (MEng)--University of Pretoria, 2013. In high range-resolution (HRR) radar systems, the returns from a single target may fall in multiple adjacent range bins which individually vary in amplitude. A target following this representation is commonly referred to as an extended target and results in more information about the target. However, extracting this information from the radar returns is challenging due to several complexities. These complexities include the single dimensional nature of the radar measurements, complexities associated with the scattering of electromagnetic waves, and complex environments in which radar systems are required to operate. There are several applications of HRR radar systems which extract target information with varying levels of success. A commonly used application is that of imaging referred to as synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging. These techniques combine multiple single dimension measurements in order to obtain a single two dimensional image. These techniques rely on rotational motion between the target and the radar occurring during the collection of the single dimension measurements. In the case of ISAR, the radar is stationary while motion is induced by the target. There are several difficulties associated with the unknown motion of the target when standard Doppler processing techniques are used to synthesise ISAR images. In this dissertation, a non-standard Dop-pler approach, based on Bayesian inference techniques, was considered to address the difficulties. The target and observations were modelled with a non-linear state space model. Several different Bayesian techniques were implemented to infer the hidden states of the model, which coincide with the unknown characteristics of the target. A simulation platform was designed in order to analyse the performance of the implemented techniques. The implemented techniques were capable of successfully tracking a randomly generated target in a controlled environment. The influence of varying several parameters, related to the characteristics of the target and the implemented techniques, was explored. Finally, a comparison was made between standard Doppler processing and the Bayesian methods proposed. gm2014 Electrical, Electronic and Computer Engineering unrestricted 2014-02-11T05:15:12Z 2014-02-11T05:15:12Z 2013-09-04 2013 Dissertation De Freitas, A 2013, A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/33372> E13/9/1028/gm http://hdl.handle.net/2263/33372 en © 2013 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Extended target Tracking High range-resolution radar High rangeresolution profile Particle filtering ISAR Particle Markov chain Monte-Carlo Particle marginal Metropolis-Hastings sampler Static parameter estimation UCTD A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar |
| title | A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar |
| title_full | A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar |
| title_fullStr | A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar |
| title_full_unstemmed | A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar |
| title_short | A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar |
| title_sort | monte carlo approach to dominant scatterer tracking of a single extended target in high range resolution radar |
| topic | Extended target Tracking High range-resolution radar High rangeresolution profile Particle filtering ISAR Particle Markov chain Monte-Carlo Particle marginal Metropolis-Hastings sampler Static parameter estimation UCTD |
| url | http://hdl.handle.net/2263/33372 |