Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpected directional jamming). In this work, we present DoA-estimation based on outlier-resistant L1-norm principal component analysis (L1-PCA) of the realified snapshots and a complete algorithmic/theoretical framework for L1-PCA of complex data through realification. Our numerical studies illustrate that the proposed DoA estimation method exhibits (i) similar performance to the conventional L2-PCA-based method, when the processed snapshots are nominal/clean, and (ii) significantly superior performance when the snapshots are faulty/corrupted.
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Direction-of-arrival estimation
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Outlier resistance
(EN)
Singular-value decomposition
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Data contamination
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Faulty measurements
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L2 norm
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Subspace data processing
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L1 norm
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Multiple signal classification
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Principal-component analysis
(EN)
Eurasip Journal on Advances in Signal Processing
(EL)
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