Direction finding with L1-norm subspaces

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.