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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...
In the past decade, there has been a growing documented effort to approximate a matrix by another of lower rank minimizing the L1-norm of the residual matrix. In this paper, we first show that the problem is NP-hard. Then, we introduce a theorem on the sparsity of the residual matrix. The theorem sets the foundation for a novel algorithm that outperforms all existing counterparts in the L1-norm error...
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...
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...
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