DOA Estimation in Heteroscedastic Noise with sparse Bayesian Learning
Keywords:
Heteroscedastic noise, sparse reconstructionAbstract
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.
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