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Cited by 968

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Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference

Open access
Yang, Zai, Xie, Lihua, Zhang, Cishen·2013·IEEE Transactions on Signal Processing

Abstract

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.

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Metadata

Authors
Yang, Z., Xie, L., & Zhang, C.
Venue
IEEE Transactions on Signal Processing
Published
2013-01
Volume
61
Issue
1
Pages
38-43
ISSN
  • 1053-587X (ISSN-L)
  • 1941-0476
OpenAlex
W2028823365
Cited by
968
References
1
FWCI
25.23
Type
journal article
Language
en
Open access
green

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Yang, Z., Xie, L., & Zhang, C. (2013). Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2012.2222378

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