Direction-of-arrival estimation by L1-norm principal components
Panos P. Markopoulos, Nicholas Tsagkarakis, Dimitris A. Pados, George N. Karystinos · 2016
Traditional subspace-based methods for direction-of-arrival (DoA) estimation rely on the L2-norm principal components (L2-PCs) of the sensor-array data. In view of the well-documented sensitivity of L2-PCs against outliers among the processed data (occurring in this case, e.g., due to intermittent directional jamming), we propose instead DoA estimation using outlier-resistant L1-norm principal components (L1-PCs) of the recorded snapshots. Our simulation studies illustrate that the proposed method exhibits (i) similar performance to conventional L2-PC-based DoA estimation, when the snapshot-data are nominal/clean, and (ii) significantly superior performance when part of the snapshots are corrupted.