@inproceedings{07b354ca4c254a929501fe458a6fade1,
title = "A robust and efficient SAR ATR algorithm using a hybrid model of fractional fourier transform and pulse coupled neural network",
abstract = "A hybrid framework consisting of Fractional Fourier Transform (FrFT) and Pulse coupled Neural network (PCNN) is proposed in this paper for highly accurate and orientation, position & scale invariant synthetic aperture radar (SAR) automatic target recognition (ATR). FrFT is used to gather scattering information and insights that are attainable using time-frequency and time-scale techniques, whereas PCNN is used to achieve invariant target recognition. Public release of the MSTAR dataset is used to validate the proposed system. We compared our proposed system performance with existing approaches and established the better performance of this system. We have shown that, even with reduced training sets, the proposed system shows consistent performance whereas the performance of conventional systems degrades.",
keywords = "ATR, FrFT, MSTAR, PCNN, SAR",
author = "Santu Sardar and Mishra, {Amit K.}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Microwave and RF Conference, IMaRC 2014 ; Conference date: 15-12-2014 Through 17-12-2014",
year = "2014",
month = dec,
day = "15",
doi = "10.1109/IMaRC.2014.7039004",
language = "English",
series = "IEEE MTT-S International Microwave and RF Conference 2014, IMaRC 2014 - Collocated with Intemational Symposium on Microwaves, ISM 2014",
publisher = "IEEE Press",
pages = "121--124",
booktitle = "IEEE MTT-S International Microwave and RF Conference 2014, IMaRC 2014 - Collocated with Intemational Symposium on Microwaves, ISM 2014",
address = "United States of America",
}