Classification of RCS sequences based on KL divergence

Classification of RCS sequences based on KL divergence

Radar cross section (RCS) is an important characteristic of radar targets. The mean, variance, skewness, kurtosis, varying patterns of RCS sequences provide rich features for radar target classification. In this study, an RCS classification method based on Kullback–Leibler divergence (KL divergence)...

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Bibliographic Details
Main Authors: Qiang Cheng, Li Chen, Yaolin Zhang
Format: Article
Language:English
Published: Wiley 2019-08-01
Series:The Journal of Engineering
Subjects:
radar target recognition
radar cross-sections
statistical distributions
signal classification
statistical analysis
pattern classification
image classification
feature extraction
learning (artificial intelligence)
kl divergence
radar cross section
kurtosis
radar target classification
rcs classification method
kullback–leibler divergence
discrete probability distributions
pre-trained distributions
ication
rcs sequences
statistical features
dynamic features
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0358

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https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0358

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