A Novel Neural Network Architecture for Radar Clutter Classification

被引：0

作者：

Eraslan, Berna ^{[1
]}

Guvensen, Gokhan M. ^{[1
]}

Tanik, Yalcin ^{[1
]}

机构：

[1] Middle East Tech Univ, Elect & Elect Engn, Ankara, Turkey

来源：

2020 IEEE 18TH WORLD SYMPOSIUM ON APPLIED MACHINE INTELLIGENCE AND INFORMATICS (SAMI 2020) | 2020年

关键词：

radar clutter; clutter classification; feed-forward neural networks; hyper-parameter selection; GROUND CLUTTER;

D O I：

10.1109/sami48414.2020.9108755

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In recent years, potential capabilities of modern radars have become tremendous, with rapid advances in electronics and software technologies. If the radar system processes received echoes based on clutter characteristics, detection performance significantly improves. To classify clutters, neural network structures are studied. A problem specific architecture, specialized in clutter classification, is designed. The design procedure of the neural network is explained with necessary theoretical background information. The performance of the network is illustrated with experimental results.

引用

页码：263 / 268

页数：6

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