Ballistic Target Classification Using Machine Learning

被引：0
|
作者：
Sukut, Mertcan [1 ]
机构：
[1] ASELSAN, Ankara, Turkiye
来源：
2023 31ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU | 2023年
关键词：
radar tracking; supervised learning; support vector machines; machine learning; target classification;
D O I：
10.1109/SIU59756.2023.10223957
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Weapon locating radars are actively being used by militaries around the world, in order to protect a predetermined area from enemy fires. They achieve this by predicting the launch and impact points of ballistic targets which they detect. One of the most important steps in predicting the launch and impact points of an incoming ballistic target is to correctly identify the type of it. In this work, a ballistic target classifier with multiple decision steps using machine-learning methods is proposed and further research possibilities in this direction are discussed.
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页数：4
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