Title
Wideband Sparse Bayesian Learning for DOA estimation from multiple snapshots.
Abstract
The directions of arrival (DOA) of plane waves are estimated from multi-frequency multi-snapshot sensor array data using Sparse Bayesian Learning (SBL). The prior for the source amplitudes is assumed to be independently zero-mean complex Gaussian distributed with hyperparameters being the unknown variances (i.e. the source powers). For a complex Gaussian likelihood with unknown noise variance hyperparameter, the corresponding Gaussian posterior distribution is derived. For a given number of DOAs, the hyperparameters are automatically selected by maximizing the evidence and promote sparse DOA estimates. The SBL scheme for DOA estimation is discussed and evaluated competitively against MUSIC.
Year
Venue
Field
2016
SAM
Wideband,Bayesian inference,Pattern recognition,Hyperparameter,Computer science,Sensor array,Posterior probability,Gaussian,Artificial intelligence,Statistics,Snapshot (computer storage),Complex normal distribution
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
11
2
Name
Order
Citations
PageRank
Peter Gerstoft18622.34
Christoph F. Mecklenbräuker238756.31