Direction-of-arrival estimation by L1-norm principal components

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 d...

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Bibliographic Details
Published in2016 IEEE International Symposium on Phased Array Systems and Technology (PAST) pp. 1 - 6
Main Authors Markopoulos, Panos P., Tsagkarakis, Nicholas, Pados, Dimitris A., Karystinos, George N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
Algorithm design and analysis
AWGN
Data contamination
Direction-of-arrival estimation
Estimation
faulty measurements
Jamming
L1 norm
L2 norm
outlier resistance
principal-component analysis
singular-value decomposition
subspace data processing
Systems operation
Two dimensional displays
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Summary: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.
DOI:10.1109/ARRAY.2016.7832585