Direction finding by complex L1-principal-component analysis

Abstract

In the light of recent developments in optimal real L1-norm principal-component analysis (PCA), we provide the first algorithm in the literature to carry out L1-PCA of complex-valued data. Then, we use this algorithm to develop a novel subspace-based direction-of-arrival (DoA) estimation method that is resistant to faulty measurements or jamming. As demonstrated by numerical experiments, the proposed algorithm is as effective as state-of-the-art L2-norm methods in clean-data environments and significantly superior when operating on corrupted data.