dc.contributor.author |
Jacobs, Jan Pieter
|
|
dc.contributor.author |
Du Plessis, W.P. (Warren Paul)
|
|
dc.date.accessioned |
2019-01-23T10:32:44Z |
|
dc.date.available |
2019-01-23T10:32:44Z |
|
dc.date.issued |
2017-11 |
|
dc.description.abstract |
An efficient technique for modeling radar cross section magnitude responses versus frequency is presented. The technique is based on Gaussian process regression and makes it possible to significantly reduce the number of expensive computer simulations required to accurately resolve these responses. Examples of two missiles are used to evaluate the proposed technique. Average predictive normalized root-mean-square errors (RMSEs) of 1.24% and 1.63% were obtained, with the worst RMSE being less than 2.2%. These results were significantly better than results obtained with alternative techniques, including geometric theory of diffraction-based modeling and support vector regression. |
en_ZA |
dc.description.department |
Electrical, Electronic and Computer Engineering |
en_ZA |
dc.description.librarian |
hj2019 |
en_ZA |
dc.description.sponsorship |
The National Research Foundation of South Africa (NRF) (Grant specific unique reference numbers (UIDs) 85845 and 103855). |
en_ZA |
dc.description.uri |
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=7727 |
en_ZA |
dc.identifier.citation |
Jacobs J.P., Du Plessis W.P. 2017, 'Efficient modeling of missile RCS magnitude responses by Gaussian processes', IEEE Antennas and Wireless Propagation Letters, vol. 16, art. 8100883, pp. 3228-3231. |
en_ZA |
dc.identifier.issn |
1536-1225 |
|
dc.identifier.other |
10.1109/LAWP.2017.2771236 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/68217 |
|
dc.language.iso |
en |
en_ZA |
dc.publisher |
Institute of Electrical and Electronics Engineers |
en_ZA |
dc.rights |
© 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission |
en_ZA |
dc.subject |
Gaussian processes (GP) |
en_ZA |
dc.subject |
Modeling |
en_ZA |
dc.subject |
Radar cross section (RCS) |
en_ZA |
dc.subject |
Computation theory |
en_ZA |
dc.subject |
Covariance matrix |
en_ZA |
dc.subject |
Gaussian distribution |
en_ZA |
dc.subject |
Gaussian noise (electronic) |
en_ZA |
dc.subject |
Mean square error |
en_ZA |
dc.subject |
Missiles |
en_ZA |
dc.subject |
Models |
en_ZA |
dc.subject |
Personnel training |
en_ZA |
dc.subject |
Computational model |
en_ZA |
dc.subject |
Geometric theory of diffractions |
en_ZA |
dc.subject |
Support vector regression (SVR) |
en_ZA |
dc.subject |
Training data |
en_ZA |
dc.subject |
Root-mean-square error (RMSE) |
en_ZA |
dc.title |
Efficient modeling of missile RCS magnitude responses by Gaussian processes |
en_ZA |
dc.type |
Postprint Article |
en_ZA |