Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference

Direction of arrival (DOA) estimation is a classical problem in signal processing with many practical applications. Its research has recently been advanced owing to the development of methods based on sparse signal reconstruction. While these methods have shown advantages over conventional ones, the...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2013-01, Vol.61 (1), p.38-43
Main Authors: Yang, Zai, Xie, Lihua, Zhang, Cishen
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Applied sciences
Bayesian analysis
Bayesian methods
Compressed sensing
Detection, estimation, filtering, equalization, prediction
Direction of arrival
Direction of arrival estimation
Estimation
Exact sciences and technology
Information, signal and communications theory
Mathematical models
Noise measurement
off-grid model
Sampling
Sampling, quantization
Signal and communications theory
Signal processing
Signal to noise ratio
Signal, noise
sparse Bayesian inference
Sparse matrices
sparse signal reconstruction
Studies
Telecommunications and information theory
Transaction processing
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Summary:Direction of arrival (DOA) estimation is a classical problem in signal processing with many practical applications. Its research has recently been advanced owing to the development of methods based on sparse signal reconstruction. While these methods have shown advantages over conventional ones, there are still difficulties in practical situations where true DOAs are not on the discretized sampling grid. To deal with such an off-grid DOA estimation problem, this paper studies an off-grid model that takes into account effects of the off-grid DOAs and has a smaller modeling error. An iterative algorithm is developed based on the off-grid model from a Bayesian perspective while joint sparsity among different snapshots is exploited by assuming a Laplace prior for signals at all snapshots. The new approach applies to both single snapshot and multi-snapshot cases. Numerical simulations show that the proposed algorithm has improved accuracy in terms of mean squared estimation error. The algorithm can maintain high estimation accuracy even under a very coarse sampling grid.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2012.2222378