Maximum Eigenvalue Based Radar Signal Detection Method for K Distribution Sea Clutter Environment

Wenjing ZHAO; Chang LIU; Wenlong LIU; Minglu JIN

doi:10.11999/JEIT171092

Volume 40 Issue 9

Aug. 2018

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Wenjing ZHAO, Chang LIU, Wenlong LIU, Minglu JIN. Maximum Eigenvalue Based Radar Signal Detection Method for K Distribution Sea Clutter Environment[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2235-2241. doi: 10.11999/JEIT171092

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Wenjing ZHAO, Chang LIU, Wenlong LIU, Minglu JIN. Maximum Eigenvalue Based Radar Signal Detection Method for K Distribution Sea Clutter Environment[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2235-2241. doi: 10.11999/JEIT171092

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Maximum Eigenvalue Based Radar Signal Detection Method for K Distribution Sea Clutter Environment

doi: 10.11999/JEIT171092

Wenjing ZHAO¹,
Chang LIU^{1, 2},
Wenlong LIU¹,
Minglu JIN^{1
,
,}

1.
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
2.
School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (61401059)

Received Date: 2017-11-20
Rev Recd Date: 2018-05-21

Available Online: 2018-07-12

Publish Date: 2018-09-01

Abstract

Abstract

Information geometry based matrix Constant False Alarm Rate (CFAR) detector is an efficient solution to the intractable issue of target detection for K-distributed sea clutter environment. However, most existing matrix CFAR detectors cost heavy computation complexity, which leads to a limitation in practical application. Based on the Neyman-Pearson criterion, the Likelihood Ratio Test (LRT) is analyzed, the relationship between LRT statistic and the Maximum Eigenvalue is derived, and Matrix CFAR Detection method based on the Maximum Eigenvalue (M-MED) is designed. Simulation results verify that the proposed method can achieve better detection performance with relatively lower computational complexity.
- CFAR detection,
- K-distribution,
- Maximum eigenvalue

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References(16)

References

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