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Self-Learning Parameter Estimation of K-Distributed Clutter Using GRU Network
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AI
DOI：10.1109/LGRS.2023.3323294.png)
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Abstract
En 中文
Robust and accurate parameter estimation of K-distributed clutter plays an important role in target detection for marine radars. The estimation performance of the existing estimators is limited by only two moments or percentiles. To break this limit, a self-learning estimator using gate recurrent unit (GRU) network is proposed to estimate shape parameter by a well-designed feature vector composed of ten moment ratios and 13 percentile ratios. It is theoretically proved that the feature vector is independent of scale parameter. Then, the scale parameter is determined by a shape-parameter-dependent percentile. Finally, it is verified by simulated data and measured data that the proposed estimator is suitable for the complicated and various clutter environments.
Keywords:
Clutter
Shape
Method of moments
Logic gates
Time series analysis
Feature extraction
Parameter estimation
Gate recurrent unit (GRU)
K distribution
parameter estimation
sea clutter
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K
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