Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Optimal prior knowledge-based direction of arrival estimation
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-6615-6583
University of Lyon, University of Saint Etienne, LASPI.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
Systems and Control Division, Uppsala University.
2012 (English)In: IET Signal Processing, ISSN 1751-9675, E-ISSN 1751-9683, Vol. 6, no 8, 731-742 p.Article in journal (Refereed) Published
Abstract [en]

In certain applications involving direction of arrival (DOA) estimation the operator may have a-priori information on some of the DOAs. This information could refer to a target known to be present at a certain position or to a reflection. In this study, the authors investigate a methodology for array processing that exploits the information on the known DOAs for estimating the unknown DOAs as accurately as possible. Algorithms are presented that can efficiently handle the case of both correlated and uncorrelated sources when the receiver is a uniform linear array. The authors find a major improvement in estimator accuracy in feasible scenarios, and they compare the estimator performance to the corresponding theoretical stochastic Cramer-Rao bounds as well as to the performance of other methods capable of exploiting such prior knowledge. In addition, real data from an ultra-sound array is applied to the investigated estimators.

Place, publisher, year, edition, pages
London: Institution of Engineering and Technology , 2012. Vol. 6, no 8, 731-742 p.
Keyword [en]
Antenna arrays, array signal processing, direction of arrival estimation, Cramer-Rao bound
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-109489DOI: 10.1049/iet-spr.2011.0453ISI: 000318231200003Scopus ID: 2-s2.0-84880004678OAI: oai:DiVA.org:kth-109489DiVA: diva2:582617
Funder
EU, FP7, Seventh Framework Programme, 228044ICT - The Next Generation
Note

QC 20130107

Available from: 2013-01-07 Created: 2013-01-05 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Exploiting Prior Information in Parametric Estimation Problems for Multi-Channel Signal Processing Applications
Open this publication in new window or tab >>Exploiting Prior Information in Parametric Estimation Problems for Multi-Channel Signal Processing Applications
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis addresses a number of problems all related to parameter estimation in sensor array processing. The unifying theme is that some of these parameters are known before the measurements are acquired. We thus study how to improve the estimation of the unknown parameters by incorporating the knowledge of the known parameters; exploiting this knowledge successfully has the potential to dramatically improve the accuracy of the estimates.

For covariance matrix estimation, we exploit that the true covariance matrix is Kronecker and Toeplitz structured. We then devise a method to ascertain that the estimates possess this structure. Additionally, we can show that our proposed estimator has better performance than the state-of-art when the number of samples is low, and that it is also efficient in the sense that the estimates have Cram\'er-Rao lower Bound (CRB) equivalent variance.

In the direction of arrival (DOA) scenario, there are different types of prior information; first, we study the case when the location of some of the emitters in the scene is known. We then turn to cases with additional prior information, i.e.~when it is known that some (or all) of the source signals are uncorrelated. As it turns out, knowledge of some DOA combined with this latter form of prior knowledge is especially beneficial, giving estimators that are dramatically more accurate than the state-of-art. We also derive the corresponding CRBs, and show that under quite mild assumptions, the estimators are efficient.

Finally, we also investigate the frequency estimation scenario, where the data is a one-dimensional temporal sequence which we model as a spatial multi-sensor response. The line-frequency estimation problem is studied when some of the frequencies are known; through experimental data we show that our approach can be beneficial. The second frequency estimation paper explores the analysis of pulse spin-locking data sequences, which are encountered in nuclear resonance experiments. By introducing a novel modeling technique for such data, we develop a method for estimating the interesting parameters of the model. The technique is significantly faster than previously available methods, and provides accurate estimation results.

Abstract [sv]

Denna doktorsavhandling behandlar parameterestimeringsproblem inom flerkanals-signalbehandling. Den gemensamma förutsättningen för dessa problem är att det finns information om de sökta parametrarna redan innan data analyseras; tanken är att på ett så finurligt sätt som möjligt använda denna kunskap för att förbättra skattningarna av de okända parametrarna.

I en uppsats studeras kovariansmatrisskattning när det är känt att den sanna kovariansmatrisen har Kronecker- och Toeplitz-struktur. Baserat på denna kunskap utvecklar vi en metod som säkerställer att även skattningarna har denna struktur, och vi kan visa att den föreslagna skattaren har bättre prestanda än existerande metoder. Vi kan också visa att skattarens varians når Cram\'er-Rao-gränsen (CRB).

Vi studerar vidare olika sorters förhandskunskap i riktningsbestämningsscenariot: först i det fall då riktningarna till ett antal av sändarna är kända. Sedan undersöker vi fallet då vi även vet något om kovariansen mellan de mottagna signalerna, nämligen att vissa (eller alla) signaler är okorrelerade. Det visar sig att just kombinationen av förkunskap om både korrelation och riktning är speciellt betydelsefull, och genom att utnyttja denna kunskap på rätt sätt kan vi skapa skattare som är mycket noggrannare än tidigare möjligt. Vi härleder även CRB för fall med denna förhandskunskap, och vi kan visa att de föreslagna skattarna är effektiva.

Slutligen behandlar vi även frekvensskattning. I detta problem är data en en-dimensionell temporal sekvens som vi modellerar som en spatiell fler-kanalssignal. Fördelen med denna modelleringsstrategi är att vi kan använda liknande metoder i estimatorerna som vid sensor-signalbehandlingsproblemen. Vi utnyttjar återigen förhandskunskap om källsignalerna: i ett av bidragen är antagandet att vissa frekvenser är kända, och vi modifierar en existerande metod för att ta hänsyn till denna kunskap. Genom att tillämpa den föreslagna metoden på experimentell data visar vi metodens användbarhet. Det andra bidraget inom detta område studerar data som erhålls från exempelvis experiment inom kärnmagnetisk resonans. Vi introducerar en ny modelleringsmetod för sådan data och utvecklar en algoritm för att skatta de önskade parametrarna i denna modell. Vår algoritm är betydligt snabbare än existerande metoder, och skattningarna är tillräckligt noggranna för typiska tillämpningar.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. xiv, 37 p.
Series
Trita-EE, ISSN 1653-5146 ; 2013:040
Keyword
Array signal processing, covariance matrix, damped sinusoids, direction of arrival estimation, frequency estimation, Kronecker, NQR, NMR, parameter estimation, persymmetric, signal processing algorithms, structured covariance estimation, Toeplitz, Array, signalbehandling, kovariansmatris, dämpad sinus, riktningbestämning, frekvensskattning, Kronecker, NQR, NMR, parameterestimering, persymmetrisk, algoritm, strukturerad kovariansmatris, Toeplitz
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-134034 (URN)978-91-7501-916-1 (ISBN)
Public defence
2013-12-06, Q2, Osquldas väg 10, KTH, Stockholm, 13:15 (English)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, 228044Swedish Research Council, 621-2011-5847
Note

QC 20131115

Available from: 2013-11-15 Created: 2013-11-15 Last updated: 2013-11-15Bibliographically approved

Open Access in DiVA

wirfalt_pledgeJ(360 kB)268 downloads
File information
File name FULLTEXT01.pdfFile size 360 kBChecksum SHA-512
a354346ee69b79ad48885ffb4077dcbec23afd933d5b6ea4d85e493a0327667e776047fd05490c1b11d5d2150364fdce47b22483ddbd60ff651430e17398712d
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

Wirfält, PetterJansson, Magnus

Search in DiVA

By author/editor
Wirfält, PetterJansson, Magnus
By organisation
Signal ProcessingACCESS Linnaeus Centre
In the same journal
IET Signal Processing
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 268 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 135 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf