NDLI: Radar emitters classification and clustering with a scale mixture of normal distributions

Content Provider	IET Digital Library
Author	Revillon, Guillaume Djafari, Ali Mohammad Enderli, Cyrille
Abstract	In this study, a scale mixture of normal distributions model is developed for classification and clustering of radar emitters. A radar signal is characterised by a pulse-to-pulse modulation pattern and is often partially observed. The proposed model can classify and cluster different radar emitters even in the presence of outliers and missing values. The classification method, based on a mixture model, focuses on the introduction of latent variables that give us the possibility to handle sensitivity of the model to outliers and to allow a less restrictive modelling of missing data. A Bayesian treatment is adopted for model learning, supervised classification and clustering. The inference is processed through a variation Bayesian approximation. Some numerical experiments on realistic data show that the proposed method provides more accurate results than state-of-the-art classification algorithms.
Starting Page	128
Ending Page	138
Page Count	11
ISSN	17518784
Volume Number	13
e-ISSN	17518792
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2018.5202
Journal	IET Radar, Sonar & Navigation
Publisher Date	2018-09-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Bayesian Approximation Bayesian Treatment Classification Method Digital Signal Processing Electrical Engineering Computing Gaussian Processes Knowledge Engineering Technique Learning in AI Mixture Model Model Learning Normal Distribution Normal Distribution Model Pattern Classification Pattern Clustering Pulse-to-pulse Modulation Pattern Radar Computing Radar Emitters Classification Radar Equipment Radar Signal Radar Signal Processing Restrictive Modelling Scale Mixture Signal Classification Signal Processing And Detection State-of-the-art Classification Algorithm Statistics Supervised Classification System And Application
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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