Realified L1-PCA for direction-of-arrival estimation: theory and algorithmsRealified L1-PCA for direction-of-arrival estimation: theory and algorithms Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2020-04-032019enSubspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpected directional jamming). In this work, we present DoA-estimation based on outlier-resistant L1-norm principal component analysis (L1-PCA) of the realified snapshots and a complete algorithmic/theoretical framework for L1-PCA of complex data through realification. Our numerical studies illustrate that the proposed DoA estimation method exhibits (i) similar performance to the conventional L2-PCA-based method, when the processed snapshots are nominal/clean, and (ii) significantly superior performance when the snapshots are faulty/corrupted.http://creativecommons.org/licenses/by/4.0/Eurasip Journal on Advances in Signal Processing20191Markopoulos_et_al_EURASIP J. Adv. Signal Process._2019(1)_2019.pdfChania [Greece]Library of TUC2020-04-02application/pdf1.8 MBfree Markopoulos, Panos Tsagkarakis Nicholas Pados Dimitris A. Karystinos Georgios Καρυστινος Γεωργιος Springer Data contamination Direction-of-arrival estimation Faulty measurements L1 norm L2 norm Multiple signal classification Outlier resistance Principal-component analysis Singular-value decomposition Subspace data processing