The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference
Open accessAbstract
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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Cited by
Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System
Huang, H., Yang, J., Huang, H., Song, Y., & Gui, G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2851783 · Cited by 818
Compressive Sensing in Electromagnetics - A Review
Massa, A., Rocca, P., & Oliveri, G. (2015) · IEEE Antennas and Propagation Magazine · doi:10.1109/map.2015.2397092 · Cited by 360
Reconfigurable Intelligent Surfaces for Localization: Position and Orientation Error Bounds
Elzanaty, A., Guerra, A., Guidi, F., & Alouini, M.-S. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3101644 · Cited by 336
Sparse methods for direction-of-arrival estimation
Yang, Z., Li, J., Stoica, P., & Xie, L. (2018) · Academic Press Library in Signal Processing, Volume 7 · doi:10.1016/b978-0-12-811887-0.00011-0 · Cited by 332
Self-citationEnhancing Sparsity and Resolution via Reweighted Atomic Norm Minimization
Yang, Z., & Xie, L. (2016) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2493987 · Cited by 304
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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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