A classification technique of group objects by artificial neural networks using estimation of entropy on synthetic aperture radar images

被引：1

作者：

Kvasnov, Anton, V ^{[1
]}

Shkodyrev, Vyacheslav P. ^{[1
]}

机构：

[1] Peter Great St Petersburg Polytech Univ SPbPU, Sch Cyberphys Syst & Control, St Petersburg 195251, Russia

来源：

JOURNAL OF SENSORS AND SENSOR SYSTEMS | 2021年 / 10卷 / 01期

关键词：

AUTOMATIC TARGET RECOGNITION;

D O I：

10.5194/jsss-10-127-2021

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

The article discusses the method for the classification of non-moving group objects for information received from unmanned aerial vehicles (UAVs) by synthetic aperture radar (SAR). A theoretical approach to analysis of group objects can be estimated by cross-entropy using a naive Bayesian classifier. The entropy of target spots on SAR images revaluates depending on the altitude and aspect angle of a UAV. The paper shows that classification of the target for three classes able to predict with fair accuracy P = 0, 964 based on an artificial neural network. The study of results reveals an advantage compared with other radar recognition methods for a criterion of the constant false-alarm rate (P-CFAR < 0:01). The reliability was confirmed by checking the initial data using principal component analysis.

引用

页码：127 / 134

页数：8

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