NDLI: Multi-frequency analysis of Gaussian process modelling for aperiodic RCS responses of a parameterised aircraft model

Content Provider	IET Digital Library
Author	Bilal, Ahmad Hamza, Syed Muhammad Taj, Ziauddin Salamat, Shuaib
Abstract	Radar cross-section (RCS) of an object is a complex function of various geometric variables, frequency and angles of incidence. In this work, an artificial intelligence solution is provided to predict the non-deterministic characteristics of RCS using the supervised machine learning algorithm that involves Gaussian process (GP) regression. A parametrised aircraft model is used to generate training data where five variables are selected as predictors while the response is chosen to be monostatic RCS in the azimuth plane. To provide a comparison of GP modelling-based predictions, shooting and bouncing rays-based multi-frequency RCS simulations are used and the results show good agreement. To further validate the GP-based modelling approach, the data of a design point is compared with the measured RCS of 1:8 scaled-down aircraft model, which confirms the accuracy of the proposed methodology. Good prediction capabilities of GP regression for RCS evaluation of complex geometries and requirement of small data set make it an excellent tool for exploring the large design space as well as integration into multi-disciplinary design optimisation environments.
Starting Page	1061
Ending Page	1067
Page Count	7
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 7, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0421
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-03-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Aperiodic RCS Responses Artificial Intelligence Solution Complex Function Electrical Engineering Computing Gaussian Process Regression Gaussian Processes Geometric Variables GP Modelling-based Predictions GP Regression Knowledge Engineering Technique Learning in AI Monostatic RCS Multidisciplinary Design Optimisation Environments Multifrequency Analysis Nondeterministic Characteristic Optimisation Optimisation Technique Parameterised Aircraft Model Parametrised Aircraft Model Radar Computing Radar Cross Section Radar Equipment RCS Evaluation Regression Analysis Shooting Statistics Supervised Machine Learning Algorithm System And Application Training Data
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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