Fishbone, Justin A.Mili, Lamine M.2024-01-242024-01-242023-03-242644-1322https://hdl.handle.net/10919/117645Many applications in signal processing require the estimation of mean and covariance matrices of multivariate complex-valued data. Often, the data are non-Gaussian and are corrupted by outliers or impulsive noise. To mitigate this, robust estimators are employed. However, existing robust estimation techniques employed in signal processing, such as M-estimators, provide limited robustness in the multivariate case. For this reason, this paper introduces the signal processing community to the highly robust class of multivariate estimators called multivariate S-estimators. The paper extends multivariate S-estimation theory to the complex-valued domain. The theoretical performances of S-estimators are explored and compared with M-estimators through the practical lens of the minimum variance distortionless response (MVDR) beamformer, and the empirical finite-sample performances of the estimators are explored through the practical lens of direction-of-arrival (DOA) estimation using the multiple signal classification (MUSIC) algorithm.Pages 208-22417 page(s)application/pdfenCreative Commons Attribution 4.0 InternationalSignal processingEstimationCovariance matricesRobustnessProbability density functionElectric breakdownSymmetric matricesComplex elliptically symmetric distributioncomplex-valued S-estimatorcovariance and shape matrix estimationrobust estimation of multivariate location and scatterSq-estimatorArticle - Refereedhttps://doi.org/10.1109/OJSP.2023.32618064Mili, Lamine [0000-0001-6134-3945]2644-1322