Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/7764
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dc.contributor.advisorNi, Q-
dc.contributor.advisorYao, W-
dc.contributor.authorLi, Wei-
dc.date.accessioned2013-12-04T15:12:39Z-
dc.date.available2013-12-04T15:12:39Z-
dc.date.issued2010-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/7764-
dc.descriptionThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.en_US
dc.description.abstractMobile wireless positioning has recently received great attention. For mobile wireless communication networks, an inherently suitable approach is to obtain the parameters that are used for positioning estimates from the radio signal measurements between a mobile device and one or more xed base stations. However, obtaining accurate estimates of these location-dependent channel parameters is a challenging task. The focus of this thesis is on the estimation of these channel parameters for mobile wireless positioning applications. In particular, we investigate novel estimators that jointly estimate more than one type of channel parameters. We rst perform a comprehensive critical review on the most recent and popular joint channel parameter estimation techniques. Secondly, we improve a state-of-the-art technique, namely the Space Alternating Generalised Expectation maximisation (SAGE) algorithm by employing adaptive interference cancellation to improve the estimation accuracy of weaker paths. Thirdly, a novel intelligent channel parameter estimation technique using Evolution Strategy (ES) is proposed to overcome the drawbacks of the existing iterative maximum likelihood methods. Furthermore, given that in reality it is di cult to obtain the number of multipath in advance, we propose a two tier Hierarchically Organised ES to jointly estimate the number of multipath as well as the channel parameters. Finally, we extend the proposed ES method to further estimate the Doppler shift in mobile environments. Our proposed intelligent joint channel estimation techniques are shown to exhibit excellent performance even with low Signal to Noise Ratio (SNR) channel conditions as well as robust against uncertainties in initialisations.en_US
dc.description.sponsorshipEPSRC and Cambridge Silicon Radioen_US
dc.language.isoenen_US
dc.publisherBrunel University School of Engineering and Design PhD Theses-
dc.relation.urihttp://bura.brunel.ac.uk/bitstream/2438/7764/1/FulltextThesis.pdf-
dc.subjectJoint channel parameter estimationen_US
dc.subjectMobile positioningen_US
dc.subjectEvolutionary algorithsen_US
dc.titleIntelligent joint channel parameter estimation techniques for mobile wireless positioning applicationsen_US
dc.typeThesisen_US
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Theses

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