Outlier-robust Tri-percentile Parameter Estimation Method of Compound-Gaussian Clutter with Inverse Gaussian Textures

SHUI Penglang; TIAN Chao; FENG Tian

doi:10.11999/JEIT211483

Volume 45 Issue 2

Feb. 2023

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SHUI Penglang, TIAN Chao, FENG Tian. Outlier-robust Tri-percentile Parameter Estimation Method of Compound-Gaussian Clutter with Inverse Gaussian Textures[J]. Journal of Electronics & Information Technology, 2023, 45(2): 542-549. doi: 10.11999/JEIT211483

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SHUI Penglang, TIAN Chao, FENG Tian. Outlier-robust Tri-percentile Parameter Estimation Method of Compound-Gaussian Clutter with Inverse Gaussian Textures[J]. Journal of Electronics & Information Technology, 2023, 45(2): 542-549. doi: 10.11999/JEIT211483

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Outlier-robust Tri-percentile Parameter Estimation Method of Compound-Gaussian Clutter with Inverse Gaussian Textures

doi: 10.11999/JEIT211483

National Laboratory of Radar Signal Processing, Xidian University Xi’an 710071, China

Funds: The National Natural Science Foundation of China (62071346)

Received Date: 2021-12-10
Accepted Date: 2022-06-01
Rev Recd Date: 2022-05-16

Available Online: 2022-06-09

Publish Date: 2023-02-07

Abstract

Abstract

Compound-Gaussian distributions with Inverse Gaussian textures (IG-CG distributions) are commonly-used model to characterize high-resolution sea clutter and its parameter estimation plays an important role in adaptive target detection in high-resolution maritime radars. In parameter estimation, sea clutter data unavoidably contain a few of outliers from radar returns of sea-surface objects and reefs and in this case outlier-robust bi-percentile estimators are one of effective methods. This paper proposes an outlier-robust Tripercentile (Tri-per) estimation method, which is an improved version of the bi-percentile estimators. The improvement is made in two aspects. The positions of two sample percentiles are optimized to improve the estimation precision of the inverse shape parameter and the third sample percentile is introduced and its position is optimized to improve the estimation precision of the scale parameter. At last, simulated and measured data are used to verify the effectiveness and robustness of the proposed tri-percentile estimators.

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References(16)

References

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