DOA M-ESTIMATION USING SPARSE BAYESIAN LEARNING

Recent investigations indicate that Sparse Bayesian Learning (SBL) is lacking in robustness. We derive a robust and sparse Direction of Arrival (DOA) estimation framework based on the assumption that the array data has a centered (zero-mean) complex elliptically symmetric (ES) distribution with finite second-order moments. In the derivation, the loss function can be quite general. We consider three specific choices: the ML-loss for the circularly symmetric complex Gaussian distribution, the ML-loss for the complex multivariate t-distribution (MVT) with nu degrees of freedom, and the loss for Huber's M-estimator. For Gaussian loss, the method reduces to the classic SBL method. The root mean square DOA performance of the derived estimators is discussed for Gaussian, MVT, and epsilon-contaminated noise. The robust SBL estimators perform well for all cases and nearly identical with classical SBL for Gaussian noise.

Automated summary

Based on the assumption of robustness, we derive a sparse direction estimation method that accurately estimates the direction of arrival in complex noise environments. The method performs well under various loss functions and has similar performance to the classical method for Gaussian noise.