Journal article
Non-Parametric Detection of the Number of Signals: Hypothesis Testing and Random Matrix Theory
IEEE Transactions on Signal Processing, Vol.57(10), pp.3930-3941
Oct/2009
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.
Details
- Title
- Non-Parametric Detection of the Number of Signals; Hypothesis Testing and Random Matrix Theory
- Creators
- Shira Kritchman (null) - 972WIS_INST___83Boaz Nadler (null) - 972WIS_INST___83
- Resource Type
- Journal article
- Publication Details
- IEEE Transactions on Signal Processing, Vol.57(10), pp.3930-3941; Oct/2009
- Number of pages
- 12
- Language
- English
- DOI
- https://doi.org/10.1109/TSP.2009.2022897
- Grant note
- Ernst Nathan biomedical fundThe work of B. Nadler was supported by a grant from the Ernst Nathan biomedical fund. Part of this work was done while B. Nadler participated in the SCH program at the Isaac-Newton Institute of Mathematical Sciences at Cambridge, U. K._ALMAME_DELIMITER_
- Record Identifier
- 993266015103596
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