The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Non-Parametric Detection of the Number of Signals: Hypothesis Testing and Random Matrix Theory
Abstract
Detection of the number of signals embedded in noise is a fundamental problem in signal and array processing. This paper focuses on the non-parametric setting where no knowledge of the array manifold is assumed. First, we present a detailed statistical analysis of this problem, including an analysis of the signal strength required for detection with high probability, and the form of the optimal detection test under certain conditions where such a test exists. Second, combining this analysis with recent results from random matrix theory, we present a new algorithm for detection of the number of sources via a sequence of hypothesis tests. We theoretically analyze the consistency and detection performance of the proposed algorithm, showing its superiority compared to the standard minimum description length (MDL)-based estimator. A series of simulations confirm our theoretical analysis.
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Patterson, N., Price, A. L., & Reich, D. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020190 · Cited by 5,280
Detection of signals by information theoretic criteria
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On the distribution of the largest eigenvalue in principal components analysis
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Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
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Cited by
The Optimal Hard Threshold for Singular Values is \(4/\sqrt {3}\)
Gavish, M., & Donoho, D. L. (2014) · IEEE Transactions on Information Theory · doi:10.1109/tit.2014.2323359 · Cited by 675
Metadata
- Authors
- Kritchman, S., & Nadler, B.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2009-10
- Volume
- 57
- Issue
- 10
- Pages
- 3930-3941
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2123226256
- Cited by
- 306
- References
- 28
- FWCI
- 27.27
- Type
- journal article
- Language
- en
- Open access
- closed
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