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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/42362066-AD79-4D95-9A0E-AC7A961890CE | - |
| Identifier | https://doi.org/10.1117/12.2053049 | - |
| Language | en | - |
| Title | Direction finding with L1-norm subspaces | en |
| Creator | Markopoulos Panagiotis | en |
| Creator | Μαρκοπουλος Παναγιωτης | el |
| Creator | Tsagkarakis N. | en |
| Content Summary | Conventional 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. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| Date of Item | 2015-11-10 | - |
| Date of Publication | 2014 | - |
| Bibliographic Citation | P. P. Markopoulos, N. Tsagkarakis, D. A. Pados, and G. N. Karystinos, “Direction finding with L1-norm subspaces,” in Proc. SPIE Compressive Sensing Conference, SPIE Defense, Security, and Sensing (DSS '14), doi:10.1117/12.2053049 | en |
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