Electrical and Electronic Engineering; Astronomy and Astrophysics
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Abstract:
We consider direction of arrival (DOA) estimation
from long-term observations in a noisy environment. In such an
environment the noise source might evolve, causing the stationary
models to fail. Therefore a heteroscedastic Gaussian noise model
is introduced where the variance can vary across observations
and sensors. The source amplitudes are assumed independent
zero-mean complex Gaussian distributed with unknown variances
(i.e., source powers), leading to stochastic maximum likelihood
(ML) DOA estimation. The DOAs are estimated from multisnapshot array data using sparse Bayesian learning (SBL) where
the noise is estimated across both sensors and snapshots.