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Direction finding with L1-norm subspaces

Markopoulos Panagiotis, Tsagkarakis N.

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URI: http://purl.tuc.gr/dl/dias/42362066-AD79-4D95-9A0E-AC7A961890CE
Year 2014
Type of Item Conference Full Paper
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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 https://doi.org/10.1117/12.2053049
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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.

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