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Self-learning parameter estimation of K-distributed clutter using nonlinear GBDT model
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  • Sainan Shi,
  • gao juan,
  • Ding Cao,
  • Yutao Zhang
Sainan Shi
Nanjing University of Information Science and Technology

Corresponding Author:snshi@nuist.edu.cn

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gao juan
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Ding Cao
Nanjing Marine Radar Institute
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Yutao Zhang
Nanjing Marine Radar Institute
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In this letter, a self-learning method using gradient boosting decision tree (GBDT) is proposed to estimate two parameters of K-distributed sea clutter. Different from the traditional methods using limited two moments or percentiles, a feature vector extracted from four moment ratios and nine percentile ratios are fully exploited by a nonlinear GBDT model, as to automatically estimate shape parameter. It is proved that the feature vector is independent of scale parameter. Then, scale parameter is determined by a shape-parameter-dependent percentile. Finally, both simulated data and measured data are used to confirm that the proposed estimator can attain robust and good performance in complicated and various clutter environments.
21 Aug 2022Submitted to Electronics Letters
21 Aug 2022Assigned to Editor
21 Aug 2022Submission Checks Completed
26 Sep 2022Reviewer(s) Assigned
09 Oct 2022Review(s) Completed, Editorial Evaluation Pending
10 Oct 2022Editorial Decision: Revise Minor