DoA Estimation Based on Matrix Preconditioning Through Sparse Reconstruction in Impulsive Noise

ZHAO Jihong; MA Zhaotian; QU Hua; WANG Weihua

doi:10.11999/JEIT170371

Volume 40 Issue 3

Mar. 2018

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ZHAO Jihong, MA Zhaotian, QU Hua, WANG Weihua. DoA Estimation Based on Matrix Preconditioning Through Sparse Reconstruction in Impulsive Noise[J]. Journal of Electronics & Information Technology, 2018, 40(3): 670-675. doi: 10.11999/JEIT170371

Citation:

ZHAO Jihong, MA Zhaotian, QU Hua, WANG Weihua. DoA Estimation Based on Matrix Preconditioning Through Sparse Reconstruction in Impulsive Noise[J]. Journal of Electronics & Information Technology, 2018, 40(3): 670-675. doi: 10.11999/JEIT170371

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DoA Estimation Based on Matrix Preconditioning Through Sparse Reconstruction in Impulsive Noise

doi: 10.11999/JEIT170371

1.
2.
(Communication and Information Engineering, Xi'an University of Posts, Xi&rsquo
3.
(Department of Information and Communication Engineering, Xi'an Jiaotong University, Xi&rsquo

Funds:

The National Natural Science Foundation of China (61372092), The Ministry of Education in the Mobile Project (MCM20150102)

Received Date: 2017-04-24
Rev Recd Date: 2017-10-23
Publish Date: 2018-03-19

Abstract

Abstract

Sparse reconstruction method suffers severe degradation in presence of impulsive noise. To deal with this problem, this paper proposed a DoA estimation method based on polynomial matrix preconditioning through sparse reconstruction. Based on the sparse reconstruction, multiply the covariance function and the direction vector by Polynomial preprocessing, which can reduce the distribution matrix of singular values, improve singular value ratio, and exhibit better sparsity. Simulation results demonstrate that the proposed algorithm achieves accurate DoA in coherent and incoherent signal sources under impulsive noise, especially have high accuracy and robustness in the heavy impulsive noise.
- DoA estimation,
- Impulsive noise,
- Sparse reconstruction,
- Polynomial preconditioning

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References(26)

References

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