The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
Abstract
This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing. The proposed approach is obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many-snapshot cases but can be used even in single-snapshot situations. SPICE has several unique features not shared by other sparse estimation methods: it has a simple and sound statistical foundation, it takes account of the noise in the data in a natural manner, it does not require the user to make any difficult selection of hyperparameters, and yet it has global convergence properties.
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Cited by
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
Sun, Y., Babu, P., & Palomar, D. P. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2601299 · Cited by 1,820
Self-citationSparsity-based DOA estimation using co-prime arrays
Zhang, Y. D., Amin, M. G., & Himed, B. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638403 · Cited by 384
Zhu, J., Xie, Y., Fu, H., Wang, C., Wang, H., Liu, Z., & Xie, Q. (2023) · Journal of Geodesy · doi:10.1007/s00190-023-01791-5 · Cited by 16
Metadata
- Authors
- Stoica, P., Babu, P., & Li, J.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2011-02
- Volume
- 59
- Issue
- 2
- Pages
- 629-638
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2115141757
- Cited by
- 614
- References
- 1
- FWCI
- 26.05
- Type
- journal article
- Language
- en
- Open access
- closed
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