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RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

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Related Experiment Video

Updated: May 8, 2026

An Ultra-clean Multilayer Apparatus for Collecting Size Fractionated Marine Plankton and Suspended Particles

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Mechanism-Data Collaboration for Characterizing Sea Clutter Properties and Training Sample Selection.

Wenhao Chen¹, Yong Zou¹, Zhengzhou Li¹

¹School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

This study introduces a novel method for maritime radar target detection by improving sea clutter characterization and training sample selection. The approach enhances detection performance and reliability in diverse marine environments.

Keywords:

maritime target detection model-data-driven multi-feature fusion sea clutter characteristics training sample selection

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Area of Science:

Maritime radar technology
Signal processing
Environmental modeling

Background:

Accurate sea clutter characterization is crucial for maritime radar target detection.
Traditional statistical models struggle with complex marine environments and imbalanced training data.
Inaccurate models and data lead to poor detection performance due to overfitting or underfitting.

Purpose of the Study:

To propose a mechanism-data collaborative method for enhanced maritime radar target detection.
To accurately characterize sea clutter variations in dynamic maritime environments.
To improve the generalization capability of radar target detectors.

Main Methods:

Utilizing the scattering coefficient as a representative feature.
Piecewise fitting classical models to measured data and fusing results to compensate for discontinuities.
Implementing a hybrid feature selection strategy integrating global density distribution and local gradient variation for training sample selection.

Main Results:

The proposed method accurately characterizes sea clutter properties across various scenarios.
A more accurate representation of sea clutter characteristics was achieved compared to traditional methods.
Detectors trained with the proposed samples demonstrated strong generalization capabilities in diverse maritime environments.

Conclusions:

Accurate sea clutter modeling and optimal training sample selection are vital for improving radar target detection.
The developed method enhances the reliability of radar-based maritime surveillance.
This work offers a robust solution for challenges in maritime radar target detection.