Semantic Feature Enhanced Graph ATtention Network for Radar Target Recognition in Heterogeneous Radar Network

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Affiliation of Author(s):BUPT

Teaching and Research Group:ICC

Journal:IEEE Sesnsors Journal

Key Words:Graph Attention Network; heterogeneous radar network; object recognition

Abstract:Radar target recognition (RTR), as a key technique of intelligent radar systems, has been widely investigated. Accurate RTR at low signal-to-noise ratios (SNRs) still remains an open challenge. Considering that most existing methods are based on a single radar or the homogeneous radar network, we extend RTR to the heterogeneous radar network in order to improve the robustness of RTR which uses the RCS signals at low SNRs by further exploiting the frequency domain information. In this paper, a Semantic Feature Enhanced Graph ATtention Network (SFE-GAT), is proposed, which extracts semantic feat

Indexed by:Journal paper

First Author:Han Meng

All the Authors:Wei Xiang,Xu Pang

Correspondence Author:Yuexing Peng,Wenbo Wang

Document Code:DOI: 10.1109/JSEN.2023.3250708

Discipline:Engineering

First-Level Discipline:信息与通信工程* Information and communication engineering

Document Type:J

Volume:23

Issue:7

Page Number:1-8

ISSN No.:1530-437X

Translation or Not:no

Date of Publication:2023-03-06

Included Journals:SCI

Links to published journals:https://ieeexplore.ieee.org/document/10061371