Aerospace and Electronic Systems Magazine July 2018 - 3

From the Associate Editor-in-Chief
JULY 2018

n July 2017, China's State
Council put out an action plan
for development of artificial
intelligence (AI) technology for
2018-2020, including research
in intelligent unmanned aerial
vehicles as well as specifically in
machine learning (ML) for other
applications. In 2018, the U.S.
Office of Naval Research put out
a special program announcement
for "Advancing Artificial IntelDavid Frederic Crouse1
ligence for the Naval Domain."
After that in 2018, the European
Commission increased its investment in AI research to 1.5 billion for the 2018-2020 timeframe.
With such a large international effort going into AI/ML research,
one can expect that the first article in this issue, which addresses
the use of neural networks to determine the relation between exhaust gas temperature and the revolutions per minute output of a
gas turbine, will be one of many in the coming years that uses an
ML technique to solve a practical problem.
Another area in which artificial intelligence is often used is
in navigation and autonomous decision making involving drones.
The second article in this issue is an in-depth survey on the use of
quad rotors, which are commonly used in small unmanned aerial
vehicles. The authors look at many aspects of such drone systems
including control, collision avoidance, and fault diagnosis.
Drones are commonly used for many imaging and sensing applications going far beyond the posting of aerial vacation footage
in a YouTube vlog, and one will generally wish to make use of all
available sensor data for sensing and navigation. The third article
This editorial introduction is supported by the Office of Naval Research
through the Naval Research Laboratory 6.1 Base Program.

JULY 2018

DOI. No. 10.1109/MAES.2018.187001

in this issue considers the joint use of using infrared and millimeter
wave sensing in a guidance system. Additionally, machine learning again plays a role in this work, as the authors make use of
convolution neural networks for target feature extraction. Though
the authors focus on the application of such a system for missile
guidance, such algorithms can be used in other guidance applications as well.
At frequencies a tenth those of a millimeter wave sensor, the
fourth article in this issue considers the passive use of Long Term
Evolution (LTE) cellular network signals for detecting changes in
the environment. Specifically, time-frequency representations of
the channel estimates are obtained using fractional Fourier transforms (FrFT) and are analyzed in different ways. One approach
illustrates the visual differences in the FrFT plots, and reference
is made to other work using neural networks to automate target
identification from such an analysis, being yet another application
of machine learning.
Moving away from terrestrial topics, with the cost of launching
a cubesat down to under $100k USD2, the advent of the space mining companies Planetary Resources and Deep Space Industries,
and the increasing number of corporations and governments operating in space, it is clear that another popular application of AI/
ML is within the aerospace industry. The final paper in this issue
considers an important topic in that field: The link between orbital
attitude estimation and dynamic control. The authors discuss how
disturbing torques and forces affecting the orientation and position
of a satellite depend on shape, orientation, and location of the satellite in space. Unlike the other papers, this is one topic where ML
and AI do not play a particularly significant role.
-David Frederic Crouse,
U.S. Naval Research Laboratory
Source: $80,000 to rideshare the smallest CubeSat on https://www.rocketlabusa.com/book-my-launch/, and $85,000 via http://nanoracks.com/
resources/faq/.

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Table of Contents for the Digital Edition of Aerospace and Electronic Systems Magazine July 2018