Signal Processing - May 2017 - 10
special reports
John Edwards
New Directions in Navigation and Positioning
Signal processing-enabled technologies pinpoint people, places, and things
I
n an era of same-day product deliveries, interplanetary space probes, and
autonomous vehicles, transporting
something-or someone-from here to
there quickly, directly, and precisely is
becoming increasingly important.
An array of navigation and positioning technologies are now available to help
guide and locate vehicles, people, and
almost endless number of objects. The
satellite-based global positioning system (GPS), for instance, now lies at the
heart of an almost endless array of location, navigation, timing, mapping, and
tracking tools. Real-time location system (RTLS) technologies, meanwhile,
rely on resources such as GPS, Wi-Fi,
Bluetooth, near-field communication
(NFC), and radio-frequency identification (RFID) to detect the current location of a target, which may be anything
from a vehicle to an item in a manufacturing plant to a person.
With navigation and positioning
technologies continuing to fuel the development of innovative commercial,
industrial, consumer, and scientific applications, researchers are turning to signal
processing methods and approaches to
tweak the performance of existing systems as well as to pioneer completely new
tools and services.
A GPS alternative
Researchers at the University of California, Riverside (UCR), have developed
Digital Object Identifier 10.1109/MSP.2017.2668526
Date of publication: 26 April 2017
10
a new navigation system that is based
entirely on existing terrestrial signals,
such as cellular and Wi-Fi, rather than
GPS. The new technology, which the
researchers claim is both highly reliable
and accurate, can function as a standalone
alternative to GPS or as an alternative to
satellite signals to enable highly reliable,
consistent, and tamper-proof navigation in
autonomous systems, such as robots, driverless terrestrial vehicles, and unmanned
aerial vehicles (UAVs).
"GPS is unreliable for anytime, anywhere navigation, including indoors
and in deep urban canyons," says team
leader Zak Kassas, an assistant professor
of electrical and computer engineering
in UCR's Bourns College of Engineering. He notes that GPS signals are also
highly vulnerable to interference, jamming, and spoofing. "However, in most
GPS-challenged environments, there
are dozens of signals of opportunity
(SOPs) that are available at various frequencies, geometry and transmission
protocols, and whose received power is
much higher than GPS," Kassas says.
By exploiting abundantly available
SOPs, the new approach reduces the sensory payload that's typically used to compensate for GPS's shortcomings. "Current
and future vehicles, whether manual,
semiautonomous or fully autonomous,
ground and aerial, would benefit from this
research," Kassas says.
The system can be used by itself
or to supplement inertial navigation
system data in the event of GPS failure. The researchers' approach include
IEEE Signal Processing Magazine
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May 2017
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theoretical analysis of SOPs in the environment, building specialized softwaredefined radios (SDRs) that can extract
relevant timing and positioning information from SOPs, developing practical
navigation algorithms and, finally, testing the system on ground vehicles and
unmanned drones.
"We have designed state-of-the-art
specialized SDRs for cellular code
division multiple access and long-term
evolution (LTE) signals," Kassas says.
"We mounted our SDRs on ground
vehicles and UAVs and demonstrated
experimentally these vehicles navigating to an unprecedented level of
accuracy only with cellular signals."
The trajectories produced by the SDRs
were within a few meters from a trajectory produced with traditional GPS
receivers (Figure 1). "To my knowledge, we were the first to demonstrate
UAVs navigating exclusively with cellular signals," Kassas remarks.
Kassas notes that the system uses signal processing in all of its stages. "We
start by studying the SOPs and deriving
theoretical signal models for what useful position-navigation-timing information we can extract from these signals,"
he says. "We then design SDRs that
process these signals and output useful
information [then] fuse the extracted
information with signals from other sensors to achieve an accurate and robust
navigation solution." The SDRs contain phase-locked loops, delay-locked
loops, frequency-locked loops, fast
Fourier transforms, inverse FFTs, and
1053-5888/17©2017IEEE
Table of Contents for the Digital Edition of Signal Processing - May 2017
Signal Processing - May 2017 - Cover1
Signal Processing - May 2017 - Cover2
Signal Processing - May 2017 - 1
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