Semi-Blind Signal Recovery in Impulsive Noise with L1-Norm PCA

Gannon, Adam; Sklivanitis, George; Markopoulos, Panos P.; Pados, Dimitris A.; Batalama, Stella N.

doi:10.1109/acssc.2018.8645515

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Recently, Markopoulos et al [47,48] calculated optimally the maximum-projection L1-PCs of real-valued data, for which up to that point only suboptimal approximations were known [36][37][38]. Experimental studies in [47][48][49][50][51][52][53] demonstrated the sturdy resistance of optimal L1-norm principal-component analysis (L1-PCA) against outliers, in various signal processing applications. Recently, [43,45] introduced a heuristic algorithm for L1-PCA that was shown to attain state-of-the-art performance/cost trade-off.…”

Section: Introductionmentioning

confidence: 99%

Realified L1-PCA for direction-of-arrival estimation: theory and algorithms

Markopoulos

Tsagkarakis

Pados

et al. 2019

EURASIP J. Adv. Signal Process.

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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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