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Multisnapshot Sparse Bayesian Learning for DOA
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DOI：10.1109/LSP.2016.2598550.png)
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Abstract
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The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source powers). For a complex Gaussian likelihood with hyperparameter, the unknown noise variance, the corresponding Gaussian posterior distribution is derived. The hyperparameters are automatically selected by maximizing the evidence and promoting sparse DOA estimates. The SBL scheme for DOA estimation is discussed and evaluated competitively against LASSO (l(1)-regularization), conventional beamforming, and MUSIC.
Keywords:
Array processing
compressive beamforming
directions of arrival (DOA) estimation
relevance vector machine
sparse reconstruction
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