DOA Estimation Via Sparse Representation of theSmoothed Array Covariance Matrix

doi:10.11999/JEIT150538

Volume 38 Issue 1

Jan. 2016

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DOA Estimation Via Sparse Representation of theSmoothed Array Covariance Matrix[J]. Journal of Electronics & Information Technology, 2016, 38(1): 168-173. doi: 10.11999/JEIT150538

Citation:

DOA Estimation Via Sparse Representation of theSmoothed Array Covariance Matrix[J]. Journal of Electronics & Information Technology, 2016, 38(1): 168-173. doi: 10.11999/JEIT150538

DOA Estimation Via Sparse Representation of theSmoothed Array Covariance Matrix[J]. Journal of Electronics & Information Technology, 2016, 38(1): 168-173. doi: 10.11999/JEIT150538

Citation:

DOA Estimation Via Sparse Representation of theSmoothed Array Covariance Matrix[J]. Journal of Electronics & Information Technology, 2016, 38(1): 168-173. doi: 10.11999/JEIT150538

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DOA Estimation Via Sparse Representation of theSmoothed Array Covariance Matrix

doi: 10.11999/JEIT150538

Funds:

The National Natural Science Foundation of China (61405150, 61271300), The Fundamental Research Funds for the Central Universities (JB140229)

Received Date: 2015-05-07
Rev Recd Date: 2015-07-08
Publish Date: 2016-01-19

Abstract

Abstract

A novel Direction-Of-Arrival (DOA) estimation algorithm based on spatial smoothing and sparse reconstruction is proposed in this paper. Firstly, the covariance matrix is processed using spatial smoothing theory, and it is converted with the Khatri-Rao transformation, then DOA estimation is achieved by sparse reconstruction of the converted matrix. Furthermore, two different kinds of methods are given to deal with the error of the objective function. Experimental results show that the proposed algorithm can reduce the amount of computation, and exhibit better performance on both coherent and non-coherent signals compared with the other DOA algorithms based on compressed sensing, especially under the conditions of low angle interval, low signal-to-noise ratio and low sampling number.
- Compressed sensing,
- Spatial smoothing,
- Sparse reconstruction,
- DOA estimation

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References(18)

References

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