Direction-of-arrival estimation by L1-norm principal componentsDirection-of-arrival estimation by L1-norm principal components Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2018-05-292017enTraditional 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.http://creativecommons.org/licenses/by/4.0/IEEE International Symposium on Phased Array Systems and Technology2016 IEEE International Symposium on Phased Array Systems and Technology Markopoulos, Panos Tsagkarakis Nicholas Pados Dimitris A. Karystinos Georgios Καρυστινος Γεωργιος Institute of Electrical and Electronics Engineers Data contamination Direction-of-arrival estimation Faulty measurements L1 norm L2 norm Outlier resistance Principal-component analysis Singular-value decomposition Subspace data processing