The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging
Abstract
Through waveform diversity, multiple-input multiple-output (MIMO) radar can provide higher resolution, improved sensitivity, and increased parameter identifiability compared to more traditional phased-array radar schemes. Existing methods for target estimation, however, often fail to provide accurate MIMO angle-range-Doppler images when there are only a few data snapshots available. Sparse signal recovery algorithms, including many$l_{1}$-norm based approaches, can offer improved estimation in that case. In this paper, we present a regularized minimization approach to sparse signal recovery. Sparse learning via iterative minimization (SLIM) follows an$l_{q}$-norm constraint (for$0
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Stable signal recovery from incomplete and inaccurate measurements
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Metadata
- Authors
- Tan, X., Roberts, W., Li, J., & Stoica, P.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2011-03
- Volume
- 59
- Issue
- 3
- Pages
- 1088-1101
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2169652966
- Cited by
- 284
- References
- 38
- FWCI
- 13.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tan, X., Roberts, W., Li, J., & Stoica, P. (2011). Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2010.2096218
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