Sparse Reconstruction Method for DOA Estimation Based on Dynamic Dictionary and Negative Exponent Penalty

QIAN Tong; CUI Wei; SHEN Qing

doi:10.1049/cje.2017.08.016

Volume 27 Issue 2

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QIAN Tong, CUI Wei, SHEN Qing, “Sparse Reconstruction Method for DOA Estimation Based on Dynamic Dictionary and Negative Exponent Penalty,” Chinese Journal of Electronics, vol. 27, no. 2, pp. 386-392, 2018, doi: 10.1049/cje.2017.08.016

Citation:

QIAN Tong, CUI Wei, SHEN Qing, “Sparse Reconstruction Method for DOA Estimation Based on Dynamic Dictionary and Negative Exponent Penalty,” Chinese Journal of Electronics, vol. 27, no. 2, pp. 386-392, 2018, doi: 10.1049/cje.2017.08.016

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Sparse Reconstruction Method for DOA Estimation Based on Dynamic Dictionary and Negative Exponent Penalty

doi: 10.1049/cje.2017.08.016

School of Information and Electronic, Beijing Institute of Technology, Beijing 100081, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61672097).

More Information

Corresponding author: CUI Wei (corresponding author) received the B.S. degree in physics and Ph.D. degree in electronics engineering from Beijing Institute of Technology, Beijing, China, in 1998 and 2003, respectively. From March 2003 to March 2005, he worked as a post-doctoral researcher in the School of Electronic and Information Engineering, Beijing Jiaotong University.He has published more than 100 papers, held 17 patents, and received the Ministerial Level Technology Advancement Award thrice. (Email:cuiwei@bit.edu.cn)
Received Date: 2017-01-16
Rev Recd Date: 2017-05-03
Publish Date: 2018-03-10

Abstract

Abstract

This paper proposes a novel sparse representation method for direction of arrival estimation based on dynamic dictionary and negative exponent penalty. The dynamic dictionary can eliminate the off-grid effect and the negative exponent penalty is capable of strengthening the sparse constraint to improve the performance. The basis is regarded as a part of the optimal target and the cross iteration is utilized to jointly update the dictionary and sparse support in this method. Based on the propositions of the penalty function, the penalty function is designed to replace of l₁ norm because of its unbiasedness and stronger sparse constraint. The regularization parameter is simplified as a constant due to pre-white process, which greatly extends the application range of the proposed method. The simulation results show that the proposed method can efficiently reduce the off-grid effect and the over-complete rate of the original dictionary. Compared with the conventional sparse representation methods, it has better performance and lower computation complexity.
- Direction of arrival (DOA),
- Sparse reconstruction,
- Dynamic dictionary,
- Penalty function

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References(26)

References

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