Direction finding with L1-norm subspacesDirection finding with L1-norm subspaces Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2015-11-102014enConventional subspace-based signal direction-of-arrival estimation methods rely on the familiar L2-norm-derived principal components (singular vectors) of the observed sensor-array data matrix. In this paper, for the first time in the literature, we find the L1-norm maximum projection components of the observed data and search in their subspace for signal presence. We demonstrate that L1-subspace direction-of-arrival estimation exhibits (i) similar performance to L2 (usual singular-value/eigen-vector decomposition) direction-of-arrival estimation under normal nominal-data system operation and (ii) significant resistance to sporadic/occasional directional jamming and/or faulty measurements.http://creativecommons.org/licenses/by-nc-nd/4.0/Proc. SPIE Compressive Sensing Conference, SPIE Defense, Security, and SensingProc. SPIE Compressive Sensing Conference, SPIE Defense, Security, and Sensing Markopoulos Panagiotis Μαρκοπουλος Παναγιωτης Tsagkarakis N.