Direction finding with L1-norm subspaces

P. P. Markopoulos; N. Tsagkarakis; D. A. Pados; G. N. Karystinos

doi:10.1117/12.2053049

23 May 2014 Direction finding with L1-norm subspaces

P. P. Markopoulos, N. Tsagkarakis, D. A. Pados, G. N. Karystinos

Author Affiliations +

Proceedings Volume 9109, Compressive Sensing III; 91090J (2014) https://doi.org/10.1117/12.2053049
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

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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P. P. Markopoulos, N. Tsagkarakis, D. A. Pados, and G. N. Karystinos "Direction finding with L1-norm subspaces", Proc. SPIE 9109, Compressive Sensing III, 91090J (23 May 2014); https://doi.org/10.1117/12.2053049

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