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Direction-of-arrival estimation by L1-norm principal components

Markopoulos, Panos, Tsagkarakis Nicholas, Pados Dimitris A., Karystinos Georgios

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URIhttp://purl.tuc.gr/dl/dias/5698F30B-0C13-48EB-B532-F2C368A03F84-
Identifierhttps://ieeexplore.ieee.org/document/7832585/-
Identifierhttps://doi.org/10.1109/ARRAY.2016.7832585-
Languageen-
TitleDirection-of-arrival estimation by L1-norm principal componentsen
CreatorMarkopoulos, Panosen
CreatorTsagkarakis Nicholasen
CreatorPados Dimitris A.en
CreatorKarystinos Georgiosen
CreatorΚαρυστινος Γεωργιοςel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryTraditional subspace-based methods for direction-of-arrival (DoA) estimation rely on the L2-norm principal components (L2-PCs) of the sensor-array data. In view of the well-documented sensitivity of L2-PCs against outliers among the processed data (occurring in this case, e.g., due to intermittent directional jamming), we propose instead DoA estimation using outlier-resistant L1-norm principal components (L1-PCs) of the recorded snapshots. Our simulation studies illustrate that the proposed method exhibits (i) similar performance to conventional L2-PC-based DoA estimation, when the snapshot-data are nominal/clean, and (ii) significantly superior performance when part of the snapshots are corrupted.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-05-29-
Date of Publication2017-
SubjectData contaminationen
SubjectDirection-of-arrival estimationen
SubjectFaulty measurementsen
SubjectL1 normen
SubjectL2 normen
SubjectOutlier resistanceen
SubjectPrincipal-component analysisen
SubjectSingular-value decompositionen
SubjectSubspace data processingen
Bibliographic CitationP. P. Markopoulos, N. Tsagkarakis, D. A. Pados and G. N. Karystinos, "Direction-of-arrival estimation by L1-norm principal components," in IEEE International Symposium on Phased Array Systems and Technology, 2017. doi: 10.1109/ARRAY.2016.7832585en

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