التفاصيل البيبلوغرافية
| العنوان: |
Robust and sparse M-estimation of DOA. |
| المؤلفون: |
Mecklenbräuker, Christoph F.1 (AUTHOR) cfm@tuwien.ac.at, Gerstoft, Peter2 (AUTHOR), Ollila, Esa3 (AUTHOR), Park, Yongsung2 (AUTHOR) |
| المصدر: |
Signal Processing. Jul2024, Vol. 220, pN.PAG-N.PAG. 1p. |
| مصطلحات موضوعية: |
*STANDARD deviations, *GAUSSIAN distribution |
| مستخلص: |
A robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order moments. The derivation allows to choose the loss function and four loss functions are discussed in detail: the Gauss loss which is the Maximum-Likelihood (ML) loss for the circularly symmetric complex Gaussian distribution, the ML-loss for the complex multivariate t -distribution (MVT) with ν degrees of freedom, as well as Huber and Tyler loss functions. For Gauss loss, the method reduces to Sparse Bayesian Learning (SBL). The root mean square DOA error of the derived estimators is discussed for Gaussian, MVT, and ϵ -contaminated data. The robust SBL estimators perform well for all cases and nearly identical with classical SBL for Gaussian array data. • SBL algorithm with general loss function for DOA M-estimation. • The CES data model includes Gaussian data as special case. • Robust and sparse DOA M-estimator is insensitive to heavy tails, outliers, and unknown source correlations. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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