The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Sparsity-Promoting Sensor Selection for Non-Linear Measurement Models
Open accessAbstract
The problem of choosing the best subset of sensors that guarantees a certain estimation performance is referred to as sensor selection. In this paper, we focus on observations that are related to a general non-linear model. The proposed framework is valid as long as the observations are independent, and its likelihood satisfies the regularity conditions. We use several functions of the Cramér-Rao bound (CRB) as a performance measure. We formulate the sensor selection problem as the design of a sparse vector, which in its original form is a nonconvex ℓ0-(quasi) norm optimization problem. We present relaxed sensor selection solvers that can be efficiently solved in polynomial time. The proposed solvers result in sparse sensing techniques. We also propose a projected subgradient algorithm that is attractive for large-scale problems. The developed theory is applied to sensor placement for localization.
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Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
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Enhancing Sparsity by Reweighted ℓ 1 Minimization
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Locating the nodes: cooperative localization in wireless sensor networks
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Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks
Gezici, S., Tian, Z., Giannakis, G. B., Kobayashi, H., Molisch, A. F., Poor, H. V., & Sahinoglu, Z. (2005) · IEEE Signal Processing Magazine · doi:10.1109/msp.2005.1458289 · Cited by 2,069
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Metadata
- Authors
- Chepuri, S. P., & Leus, G.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2015-02
- Volume
- 63
- Issue
- 3
- Pages
- 684-698
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2079271562
- Cited by
- 288
- References
- 24
- FWCI
- 24.84
- Type
- journal article
- Language
- en
- Open access
- green
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