Unmanned
Aircraft System Navigation in the Urban Environment: A Systems Analysis
Journal
of Aerospace Information Systems
This
article from the Journal of Aerospace Information Systems analyzes alternative
methods for Unmanned Aerospace Systems (UAS) navigation within urban
environments. Navigation accuracy by Global
Positioning System (GPS) is severely degraded due to urban canyons, where
accuracy is particularly poor. An urban
canyon is best described as area flanked by tall buildings. Although Global Navigation Satellite System (GNSS)
is unreliable in the vicinity of dense urban structure it can be used in
combination with other complimentary sensors to provide position and velocity
measurement.
Urban
UAS missions related to law enforcement, traffic surveillance, riot control,
and anti-terrorism are all challenged by the physical obstacles and
communication and navigation issues.
There is a need for a more accurate mode of navigation in GPS denied
area. Navigation of a UAS is
accomplished through multiple exteroceptive sensors that gather various types
of information about the environment.
Navigation can also be considered a proprioceptive concept because the
unmanned system is becoming aware of its own relative position in space.
GPS
and IMU
An
Inertial Measurement Unit (IMU) consisting of gyroscopes, accelerometers, and
magnetometer provides measurements of angular velocity gravity and magnetic
north vectors (Rufa, 2016). An advantage
of IMU for navigation is that measurements are based on inertial accelerations
and not affected by the urban structure. The disadvantage the IMU is attitude
drift of approximate position. However
when GPS and IMU are combined, UAS position, velocity and attitude can be more accurately
achieved. To get an idea of how
inaccurate GPS alone is in an urban environment, a study was done in Hong Kong;
when GPS was available, the accuracy was worse than 20 meters for 40% of the
points and worse than 100 meters for 9% of the points.
Computer
Vision
Computer
vision for navigation is a method that is still actively being researched. Here, in a well-lit environment, computer
vision provides information to a filter that can generate position, airspeed
and attitude measurements. Unfortunately
these sensors are limited to high contrast and well lit environments. How does this work? How can computer vision provide location
information for a UAS?
“Optical
flow is defined as the distribution of apparent velocities of brightness
pattern in an image (Rufa, 2016).”
Optical flow is calculated by “comparing pixels in sequential images to
determine the local velocity” to determine the velocity of the UAS camera that
is capturing the images.
Air
Data
Air data via
static pressure and dynamic port system generates airspeed and altitude
measurements like passenger aircraft. An
advantage with an air data system on an UAS in an urban environment is that it
provides navigation data independent from other sensors but it will not always
be reliable in an urban environment with shifting winds and gusts that occur in
the vicinity of urban structures.
Long-Term
Evolution (LTE)
Long-Term
Evolution (LTE) can provide location data from a cellular network that can
increase accuracy of navigation in urban terrain. The FCC’s 911 system requires that cellular
carriers meet accuracy for phone location within 300 meters. These locations for geotagging can be as
accurate as 3 to 31 meters. LTE positioning is comprised of 3 techniques:
enhanced cellular identification (E-CID), observed time difference of arrival
(OTDOA) and assisted global navigation satellite systems (A-GNSS). These methods for location within a cellular
network are independent of GPS and are in an active area of research for urban
navigation.
In
this study the different techniques of navigation sensors went through a
simulated operation and were characterized and evaluated. Vision provided an airspeed measurement but
it needed to be augments with an IMU or other inertial position
measurement. LTE was not sufficient in
horizontal position. LTE experiences a 4
second delay. If the delay is
substantially decreased it may boost accuracy.
The researchers will continue to explore LTE as a means of
navigation. This article was informative
on the types of sensors used for an UAS navigating in an urban environment and
analyzed how sensors complement one another to yield an accurate measurement of
location and movement.
References:
Rufa,
J. R., & Atkins, E. M. (2016). Unmanned aircraft system navigation in the
urban environment: A systems analysis. Journal of Aerospace Information
Systems, 13(4), 143-160. doi:10.2514/1.I010280
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