468 research outputs found
Robust and Sparse M-Estimation of DOA
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
-distribution (MVT) with 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 -contaminated data. The robust SBL
estimators perform well for all cases and nearly identical with classical SBL
for Gaussian noise
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