Signal Processing - September 2016 - 117

Discussion and concluding remarks
This article presented a review of recent developments in
mmW imaging based on computational imaging methods for
security and surveillance applications. We believe that recent
advances in computational imaging have brought substantial
opportunities to mmW imaging. We hope that the survey
helped guide the interested reader through the extensive literature. It does not cover all the literature on mmW and computational imaging, so we have chosen to focus on a subset of
work that reflects some of the most recent progress.
A number of challenges and issues commonly confront
mmW imaging technology. Computational imaging methods
may prove useful in addressing some of these challenges. The
following are several examples:
■ Affordability. The technology readiness level of mmW
devices is immature compared to optical and infrared
arrays. The lack of readily available and affordable sources
and detection technology has resulted in comparatively
small arrays (kilopixels or fewer) and a tradeoff between
the number of achievable image pixels and the desire to
rapidly image wide fields of regard with high angular resolution. MmW compressed sensing has shown promising
results in reducing the overall number of scene observations
needed to reconstruct an image. Perhaps these techniques or
other computational imaging methods could help curb the
cost of mmW systems by requiring fewer detector elements
to realize an imaging capability that is more comparable to
what could be achieved with a larger-format array.
■ SWaP. Many mmW imaging systems are not viable for
deployment across a broad variety of platforms that
would benefit from their use. Compound antenna systems
and lens-based imagers, for example, scale volumetrically. To achieve a high resolution and a wide field of view,
larger apertures and mechanical scanners, which have
important implications for SWaP, are usually used. These
solutions do not tend to be man-portable, for example.
Additionally, for broad applicability, one also wants platform-agnostic solutions that do not require specific
aspects of the platform to form images, like platform
motion, for example. Computational imagers may offer
some key advantages, like the distributed aperture mmW
imaging technology discussed in the "Computational
mmW imaging approaches," which scales in 2-D versus
3-D, for example.
■ Surveillance of moving targets. Imaging of moving targets
with high resolution and high frame rates can be challenging with existing systems. At lower frequency, SAR offers
excellent atmospheric penetration properties but relatively
slow frame rates. MmW imagers can be limited by the
speed of mechanical scanners, and electronic beam-scanning technology is immature and costly at millimeter
wavelengths. Given challenges like these, perhaps computational imaging techniques could be applied to help compensate for image blur with existing systems.
Computational mmW imaging promises to be an active area
of research. However, little is known about the quantitative

performance advantage of computational imaging methods for
mmW imaging. We expect that derivation of the performance
bounds for various computational mmW imaging methods
will produce stronger guidance to developing more advanced
mmW imaging modalities, which will have a wider spectrum
of applications in surveillance, defense, and aviation problems.

Authors
Vishal M. Patel (vishal.m.patel@rutgers.edu) received his B.S.
degrees in electrical engineering and applied mathematics
(Hons.); his M.S. degree in applied mathematics from North
Carolina State University, Raleigh, in 2004 and 2005, respectively; and his Ph.D. degree in electrical engineering from the
University of Maryland, College Park, in 2010. He is currently an assistant professor in the Department of Electrical and
Computer Engineering at Rutgers University. Prior to joining
Rutgers, he was a member of the research faculty with the
University of Maryland Institute for Advanced Computer
Studies, College Park. His current research interests include
signal processing, computer vision, and pattern recognition
with applications in biometrics and imaging. He is a recipient
of the 2016 Office of Naval Research Young Investigator
Award and the 2010 Oak Ridge Associated Universities Postdoctoral Fellowship. He is a member of Eta Kappa Nu, Pi Mu
Epsilon, and Phi Beta Kappa.
Joseph N. Mait (joseph.n.mait2.civ@mail.mil) received his
Ph.D. degree from the Georgia Institute of Technology in 1985.
He is the chief scientist of the U.S. Army Research Laboratory.
He is a fellow of the Society of Photo-Instrumentation
Engineers and the Optical Society of America (OSA) and a
Senior Member of the IEEE. He is the immediate past editor-inchief of OSA's Applied Optics. In 2014, he was awarded a
Presidential Rank Award for Meritorious Senior Professionals.
His research interests include sensors and the application of
optics, photonics, and electromagnetics to sensing and sensor
signal processing.
Dennis W. Prather (dprather@udel.edu) received his
Ph.D. degree from the University of Maryland in 1997. He
is an Endowed Professor of Electrical Engineering at the
University of Delaware. He is a Senior Member of the
IEEE, fellow of the Society of Photo-Instrumentation
Engineers, and a fellow of the Optical Society of America.
His research focuses on both the theoretical and experimental aspects of RF-photonic elements and their integration
into various systems for imaging, communications and
radar. He has authored or coauthored more than 400 scientific papers, holds more than 40 patents, and has written ten
books/book-chapters.
Abigail S. Hedden (abigail.s.hedden.civ@mail.mil)
received her Ph.D. degree in physics from the University of
Arizona in 2007. She is a physicist in the RF Technology and
Integration Branch of the Sensors and Electron Devices
Directorate at the U.S. Army Research Laboratory in Adelphi,
Maryland. Her current research interests include development
of millimeter-wave instrumentation and radar systems, phenomenology, and experimentation.

IEEE SIgnal ProcESSIng MagazInE

September 2016

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Table of Contents for the Digital Edition of Signal Processing - September 2016