Direction Finding with L1-norm Subspaces

被引：8

作者：

Markopoulos, P. P. ^{[1
]}

Tsagkarakis, N. ^{[1
]}

Pados, D. A. ^{[1
]}

Karystinos, G. N. ^{[2
]}

机构：

[1] SUNY Buffalo, Dept Elect Engn, Buffalo, NY 14260 USA

[2] Tech Univ Crete, Sch Elect & Comp Engn, Khania 73100, Greece

来源：

COMPRESSIVE SENSING III | 2014年 / 9109卷

关键词：

Dimensionality reduction; direction-of-arrival estimation; erroneous data; faulty measurements; jamming; L-1; norm; L-2; principal-component analysis; outlier resistance; subspace signal processing; MAXIMUM-LIKELIHOOD; WIDE-BAND; LOCALIZATION; MUSIC;

D O I：

10.1117/12.2053049

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Conventional subspace-based signal direction-of-arrival estimation methods rely on the familiar L-2-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 L-1-norm maximum projection components of the observed data and search in their subspace for signal presence. We demonstrate that L-1-subspace direction-of-arrival estimation exhibits (i) similar performance to L-2 (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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页数：11

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