DOA Estimation in Heteroscedastic Noise with sparse Bayesian Learning

Authors

  • Peter Gerstoft NoiseLab, UCSD La Jolla, USA
  • Christoph F. Mecklenbrauker Inst. of Telecommunications TU Wien Vienna, Austria
  • Santosh Nannuru IIIT Hyderabad, SPCRC, IIIT Hyderabad Hyderabad, India
  • Geert Leus Dept. of Electrical Eng., Delft Univ. of Technology Delft, Netherlands

Keywords:

Heteroscedastic noise, sparse reconstruction

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.

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Published

2020-11-07

How to Cite

Peter Gerstoft, Christoph F. Mecklenbrauker, Santosh Nannuru, & Geert Leus. (2020). DOA Estimation in Heteroscedastic Noise with sparse Bayesian Learning. The Applied Computational Electromagnetics Society Journal (ACES), 35(11), 1439–1440. Retrieved from https://journals.riverpublishers.com/index.php/ACES/article/view/7641

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