DOA Estimation Based on Sparse Representation of the Fractional Lower Order Statistics in Impulsive Noise

Sen Li; Rongxi He; Bin Lin; Fei Su

doi:10.1109/JAS.2016.7510187

Volume 5 Issue 4

Jul. 2018

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2018 > 5(4): 860-868

Sen Li, Rongxi He, Bin Lin and Fei Su, "DOA Estimation Based on Sparse Representation of the Fractional Lower Order Statistics in Impulsive Noise," IEEE/CAA J. Autom. Sinica, vol. 5, no. 4, pp. 860-868, July 2018. doi: 10.1109/JAS.2016.7510187

Citation:

Sen Li, Rongxi He, Bin Lin and Fei Su, "DOA Estimation Based on Sparse Representation of the Fractional Lower Order Statistics in Impulsive Noise," IEEE/CAA J. Autom. Sinica, vol. 5, no. 4, pp. 860-868, July 2018. doi: 10.1109/JAS.2016.7510187

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DOA Estimation Based on Sparse Representation of the Fractional Lower Order Statistics in Impulsive Noise

doi: 10.1109/JAS.2016.7510187

Department of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

Funds:

the National Natural Science Foundation of China 61301228

the National Natural Science Foundation of China 61371091

the Fundamental Research Funds for the Central Universities 3132014212

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Author Bio:
Sen Li received the B.S. degree in microelectronics from Liaoning University, Shenyang, China, in 1996, the M.S. degree in information and communication engineering from Dalian Maritime University, Dalian, China in 1999 and the Ph.D. degree in information and signal processing from Dalian University of Technology, Dalian, China in 2011. In 2013, she held a postdoctoral position with the Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada. She is currently an Associate Professor with the Department of Information Science and Technology, Dalian Maritime University. Her research interests include spectral estimation, array signal processing, communication signal processing, statistics and adaptive signal processing and non-Gaussian signal processing.(e-mail: listen@dlmu.edu.cn)

Rongxi He (M'08) received the B.S. and M.S. degrees from Dalian Maritime University in 1992 and 1995, respectively, and the Ph.D. degree from University of Electronic Science and Technology of China in 2002. He is currently a Professor with the Department of Information Science and Technology, Dalian Maritime University. His research interests include wireless communication and networks. He is a senior member of China Computer Federation (CCF), and a member of IEEE.(e-mail: hrx@dlmu.edu.cn)

Bin Lin (M'10) received the B.Sc. degree in computer communications and the M.Sc. degree in computer science from Dalian Maritime University, Dalian, China, in 1999 and 2003, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Waterloo, Waterloo, ON, Canada, in 2009. In 2009, she held a postdoctoral position with the Department of Electrical and Computer Engineering, University of Waterloo. She is currently a Professor with the Department of Information Science and Technology, Dalian Maritime University. Her research interests include wireless communications, wireless/optical network integration, network dimensioning and optimization, location problems, resource allocation, artificial intelligence, pattern recognition, artificial neural networks, and marine remote sensing.(e-mail: binlin@dlmu.edu.cn)

Fei Sun received the B.S. degree in electronic information engineering from Chuzhou College in 2013 and the M.S. degree in electronic and communication engineering from Dalian Maritime University in 2016. His research interests include array signal processing and compressed sensing.(e-mail: sunfei@dlmu.edu.cn)
Corresponding author: Rongxi He, e-mail: hrx@dlmu.edu.cn
Received Date: 2015-09-11
Accepted Date: 2016-02-18

Abstract

Abstract

This paper is mainly to deal with the problem of direction of arrival (DOA) estimations of multiple narrow-band sources impinging on a uniform linear array under impulsive noise environments. By modeling the impulsive noise as fistable distribution, new methods which combine the sparse signal representation technique and fractional lower order statistics theory are proposed. In the new algorithms, the fractional lower order statistics vectors of the array output signal are sparsely represented on an overcomplete basis and the DOAs can be effectively estimated by searching the sparsest coefficients. To enhance the robustness performance of the proposed algorithms, the improved algorithms are advanced by eliminating the fractional lower order statistics of the noise from the fractional lower order statistics vector of the array output through a linear transformation. Simulation results have shown the effectiveness of the proposed methods for a wide range of highly impulsive environments.
- α-stable distribution,
- direction of arrival (DOA),
- fractional lower-order statistics,
- impulsive noise,
- sparse representation

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References(24)

References

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DOA Estimation Based on Sparse Representation of the Fractional Lower Order Statistics in Impulsive Noise

doi: 10.1109/JAS.2016.7510187

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