Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Efficient multi-agent communication via entity-aware causal network.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

ImVoxelGNet: Image to voxels geometry-aware projection for multi-view RGB-based 3D object detection.

PloS one·2025

Same author

IFKD: Implicit field knowledge distillation for single view reconstruction.

Mathematical biosciences and engineering : MBE·2023

Same author

Adaptive Points Sampling for Implicit Field Reconstruction of Industrial Digital Twin.

Sensors (Basel, Switzerland)·2022

Same author

Improved Panoramic Representation via Bidirectional Recurrent View Aggregation for Three-Dimensional Model Retrieval.

IEEE computer graphics and applications·2018

Same author

Removal of clock gene <i>Bmal1</i> from the retina affects retinal development and accelerates cone photoreceptor degeneration during aging.

Proceedings of the National Academy of Sciences of the United States of America·2018

Search research articles

Related Experiment Video

Updated: May 27, 2026

Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels

Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels

Published on: September 8, 2016

Scattering center guided mono-static radar cross section prediction.

Zehao Tang¹, Yuxuan Luo¹, Shuo Zhang²

¹School of Computer Science and Engineering, Beihang University, Beijing, 100191, China.

Neural Networks : the Official Journal of the International Neural Network Society

|May 25, 2026

Summary

This study introduces a new method for radar cross section (RCS) estimation that accounts for both object shape and surface details. The Scattering Center guided RCS Network (SC-RNet) improves electromagnetic computation accuracy by analyzing multi-scale features.

Keywords:

Deep learning Feature fusion Hierarchical features Mono-static radar cross section

More Related Videos

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Scattering And Absorption of Light in Planetary Regoliths

Scattering And Absorption of Light in Planetary Regoliths

Published on: July 1, 2019

Related Experiment Videos

Last Updated: May 27, 2026

Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels

Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels

Published on: September 8, 2016

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Scattering And Absorption of Light in Planetary Regoliths

Scattering And Absorption of Light in Planetary Regoliths

Published on: July 1, 2019

Area of Science:

Electromagnetics
Computational Physics
Machine Learning

Background:

Radar cross section (RCS) is crucial for characterizing radar signal scattering.
Existing learning-based methods often neglect fine surface details, limiting RCS estimation accuracy.
Both global geometry and surface details significantly impact RCS.

Purpose of the Study:

To develop a novel method for accurate RCS estimation that incorporates both global structure and fine surface details.
To enhance learning-based electromagnetic computation by addressing the limitations of existing approaches.
To improve the modeling of multi-scale information relevant to RCS.

Main Methods:

Proposed a novel method incorporating scattering center theory to decouple geometric features into structural shape and fine surface detail representations.
Developed the Scattering Center guided RCS Network (SC-RNet) for extracting and fusing shallow (local textures) and deep (overall structures) features.
Conducted experimental evaluations and ablation studies to validate the proposed method and its modules.

Main Results:

SC-RNet demonstrates improved modeling of local surface variations' influence on RCS.
The network achieves consistent and reliable prediction performance in both qualitative and quantitative evaluations.
Ablation studies confirmed the effectiveness of individual modules within SC-RNet.

Conclusions:

The proposed SC-RNet effectively captures multi-scale information by explicitly representing geometric features.
The method enhances the accuracy and reliability of RCS estimation, particularly concerning surface details.
SC-RNet represents a significant advancement in learning-based electromagnetic computation for RCS analysis.