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Continuous sparse recovery for direction of arrival estimation with co-prime arrays

Authors: Zhao Tan; Arye Nehorai; Yonina C. Eldar;

Continuous sparse recovery for direction of arrival estimation with co-prime arrays

Abstract

We consider the problem of direction of arrival (DOA) estimation using a newly proposed structure of co-prime arrays. A continuous sparse recovery method is implemented in order to increase resolution. We show that in the noiseless case one can theoretically detect up to MN/2 sources with only 2M+N sensors via continuous sparse recovery. The noise statistics of co-prime arrays are also analyzed to demonstrate the robustness of the proposed optimization scheme. Using numerical examples, we show the superiority of the proposed method.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Top 10%
Average
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