Learning-based space-time adaptive processing

TLDR: It is shown that the proposed Learning-Based Space-Time Adaptive Processing (LBSTAP) technique not only reduces the need for a large, usually unavailable, homogenous target-free set of range bins, but also provides better performance in terms of Doppler side-lobe-level reduction.

Abstract: Space-time adaptive processing (STAP) has been used for many years in moving target indicator (MTI) radars to solve the problem of target detection in presence of an interfering environment. Over the years, different versions of STAP have been introduced to enhance its performance and overcome its practical difficulties. In this work, we introduce a new method for target detection and localization in which the need for a large homogenous target-free set of training range bins - which is traditionally used to estimate the interference covariance matrix - is reduced by the use of regression methods and pattern classification techniques to train over the 2D spatial-temporal space. It is shown that the proposed Learning-Based Space-Time Adaptive Processing (LBSTAP) technique not only reduces the need for a large, usually unavailable, homogenous target-free set of range bins, but also provides better performance in terms of Doppler side-lobe-level reduction.

Figures

Figure 11 Classifier design

Figure 8 STAP summary

Figure 2 Array elements

Figure 15 Unprocessed data

Figure 13 Polynomial classifier training

Figure 28 STAP output (clutter-variation)

Full text

LEARNING-BASED SPACE-TIME ADAPTIVE PROCESSING
by
Alaa El Khatib
A Thesis Presented to the Faculty of the
American University of Sharjah
College of Engineering
in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science in
Electrical Engineering
Sharjah, United Arab Emirates
June 2013

© 2013 Alaa El Khatib. All rights reserved.

Approval Signatures
We, the undersigned, approve the Master’s Thesis of Alaa El Khatib.
Thesis Title: Learning-Based Space-Time Adaptive Processing
Signature Date of Signature
(dd/mm/yyyy)
___________________________ _______________
Dr. Hasan S.Mir
Assistant Professor, Department of Electrical Engineering
Thesis Advisor
___________________________ _______________
Dr. Khaled Assaleh
Professor, Department of Electrical Engineering
Thesis Co-Advisor
___________________________ _______________
Dr. Mohammed El-Tarhuni
Associate Professor, Department of Electrical Engineering
Thesis Committee Member
___________________________ _______________
Dr. Naoufel Werghi
Associate Professor, Department of Electrical Engineering
Khalifa University (Sharjah Campus)
Thesis Committee Member
___________________________ _______________
Dr. Mohammed El-Tarhuni
Head
Department of Electrical Engineering
___________________________ _______________
Dr. Hany El-Kadi
Associate Dean
College of Engineering
___________________________ _______________
Dr. Leland Blank
Interim Dean
College of Engineering
___________________________ _______________
Dr. Khaled Assaleh
Director of Graduate Studies

Acknowledgments
After giving due praise to our Lord, the Most Merciful, for all His blessings, I
would like to thank my thesis advisors, Dr. Hasan Mir and Dr. Khaled Assaleh, for their
support and guidance through the completion of this thesis. I also would like to thank the
department of Electrical Engineering at the American University of Sharjah for providing
me with the opportunity to be part of their graduate program. Last but not least, I would
like to express my gratitude to my parents, family and friends for the undeserved support
and compassion with which they overwhelm me all the time.

To those unwilling
to give up on their dreams…