Array and multichannel signal processing using nonparametric statistics

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Visuri, Samuli
dc.date.accessioned 2012-02-13T12:18:31Z
dc.date.available 2012-02-13T12:18:31Z
dc.date.issued 2001-03-02
dc.identifier.isbn 951-22-5364-X
dc.identifier.issn 1456-6907
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2324
dc.description.abstract In array signal processing a group of sensors located at distinct spatial locations is deployed to measure a propagating wavefield. The multichannel output is then processed to provide information about parameters of interest. Application areas include smart antennas in communications, radar, sonar and biomedicine. When deriving array signal processing algorithms the noise is typically modeled as a white Gaussian random process. A shortcoming of the estimation procedures derived under Gaussian assumption is that they are extremely sensitive to deviations from the assumed model, i.e. they are not robust. In real-world applications the assumption of white Gaussian noise is not always valid. Consequently, there has been a growing interest in estimation methods which work reliably in both Gaussian and non-Gaussian noise. In this thesis, new statistical procedures for array and multichannel signal processing are developed. In the area of array signal processing, the work concentrates on high-resolution subspace-based Direction Of Arrival (DOA) estimation and estimation of the number of source signals. Robust methods for DOA estimation and estimation of the number of source signals are derived. Spatial-smoothing based extensions of the techniques to deal with coherent signals are also derived. The methods developed are based on multivariate nonparametric statistics, in particular sign and rank covariance matrices. It is shown that these statistics may be used to obtain convergent estimates of the signal and noise subspaces for a large family of symmetric noise distributions. Simulations reveal that the techniques developed exhibit near-optimal performance when the noise distribution is Gaussian and are highly reliable if the noise is non-Gaussian. Multivariate nonparametric statistics are also applied to frequency estimation and estimation of the eigenvectors of the covariance matrix. Theoretical justification for the techniques is shown and their robust performance is illustrated in simulations. en
dc.format.extent 88, [76]
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Report / Helsinki University of Technology, Signal Processing Laboratory en
dc.relation.ispartofseries 31 en
dc.relation.haspart S. Visuri, V. Koivunen and H. Oja. Sign and rank covariance matrices. Journal of Statistical Planning and Inference, vol. 91, no. 2, pp. 557-575, 2000.
dc.relation.haspart S. Visuri, H. Oja and V. Koivunen. Multichannel signal processing using spatial rank covariance matrices. In Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP'99), vol. 1, pp. 75-79, (Antalya, Turkey), June 1999.
dc.relation.haspart S. Visuri, H. Oja and V. Koivunen. Robust subspace DoA estimation for wireless communications. In Proceedings of the 2000 IEEE 51st Vehicular Technology Conference (VTC2000-Spring), vol. 3, pp. 2551-2555, (Tokyo, Japan), May 2000.
dc.relation.haspart S. Visuri, H. Oja and V. Koivunen. Nonparametric statistics for DOA estimation in the presence of multipath. In Proceedings of the First IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2000), pp. 356-360, (Cambridge, MA, USA), March 2000.
dc.relation.haspart S. Visuri, H. Oja and V. Koivunen. Subspace-based direction of arrival estimation using nonparametric statistics. Helsinki University of Technology Signal Processing Laboratory, Report 30, 2000. IEEE Transactions on Signal Processing, accepted for publication with minor revision.
dc.relation.haspart S. Visuri, H. Oja and V. Koivunen. Nonparametric statistics for subspace based frequency estimation. In Proceedings of the X European Signal Processing Conference (EUSIPCO-2000), vol. 3, pp. 1261-1264, (Tampere, Finland), September 2000.
dc.subject.other Electrical engineering en
dc.title Array and multichannel signal processing using nonparametric statistics en
dc.type G5 Artikkeliväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Electrical and Communications Engineering en
dc.contributor.department Sähkö- ja tietoliikennetekniikan osasto fi
dc.subject.keyword multichannel signal processing en
dc.subject.keyword nonparametric statistics en
dc.subject.keyword array signal processing en
dc.subject.keyword signal modeling en
dc.subject.keyword robust estimation en
dc.subject.keyword DOA en
dc.subject.keyword subspace-based direction of arrival en
dc.identifier.urn urn:nbn:fi:tkk-002695
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.contributor.lab Signal Processing Laboratory en
dc.contributor.lab Signaalinkäsittelytekniikan laboratorio fi


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