In a recent study, researchers from the University of California, Riverside develop a highly reliable and accurate navigation system.
The system can exploit existing environmental signals such as cellular and Wi-Fi, rather than the Global Positioning System (GPS).
This technology can be used as a standalone alternative to GPS, or complement current GPS-based systems to enable highly reliable, consistent, and tamper-proof navigation.
It can also be used to develop navigation systems that meet the stringent requirements of fully autonomous vehicles, such as driverless cars and unmanned drones.
The finding was presented at the 2016 Institute of Navigation Global Navigation Satellite System Conference in Portland.
The two studies, “Signals of Opportunity Aided Inertial Navigation” and “Performance Characterization of Positioning in LTE Systems,” both won best paper presentation awards.
Nowadays, most navigation systems in cars and portable electronics use the space-based Global Navigation Satellite System (GNSS).
This can include the U.S. system GPS, Russian system GLONASS, European system Galileo, and Chinese system Beidou.
For precision technologies, such as aerospace and missiles, navigation systems typically combine GPS with a high-quality on-board Inertial Navigation System (INS).
The INS delivers a high level of short-term accuracy but eventually drifts when it loses touch with external signals.
Despite advances in this technology, current GPS/INS systems will not meet the demands of future autonomous vehicles for several reasons.
First, GPS signals alone are extremely weak and unusable in certain environments like deep canyons.
Second, GPS signals are susceptible to intentional and unintentional jamming and interference.
Third, civilian GPS signals are unencrypted, unauthenticated, and specified in publicly available documents, making them spoofable (i.e., hackable).
Current trends in autonomous vehicle navigation systems therefore rely not only on GPS/INS, but a suite of other sensor-based technologies such as cameras, lasers, and sonar.
By adding more and more sensors, scientists are throwing ‘everything but the kitchen sink’ to prepare autonomous vehicle navigation systems for the inevitable scenario that GPS signals become unavailable.
In the current study, the team took a different approach, which exploits signals that are already out there in the environment.
Instead of adding more internal sensors, the researchers have been developing autonomous vehicles that can tap into the hundreds of signals around it at any point in time, like cellular, radio, television, Wi-Fi, and other satellite signals.
In the conference, they showcased ongoing research that exploits these existing communications signals, called “signals of opportunity (SOP)” for navigation.
The system can be used by itself, or, more likely, to supplement INS data in the event that GPS fails.
The team’s end-to-end research approach includes theoretical analysis of SOPs in the environment, building specialized software-defined radios (SDRs) that will extract relevant timing and positioning information from SOPs, developing practical navigation algorithms, and finally testing the system on ground vehicles and unmanned drones.
The researchers suggest that autonomous vehicles will inevitably result in a socio-cultural revolution.
The team is addressing the challenges associated with realizing practical, cost-effective, and trustworthy autonomous vehicles.
Their overarching goal is to get these vehicles to operate with no human-in-the loop for prolonged periods of time, performing missions such as search, rescue, surveillance, mapping, farming, firefighting, package delivery, and transportation.
Citations: Morales JJ, et al. (2016). Signals of Opportunity Aided Inertial Navigation. Paper presented at the 2016 Institute of Navigation Global Navigation Satellite System Conference.
Shamaei K, et al. (2016). Performance Characterization of Positioning in LTE Systems. Paper presented at the 2016 Institute of Navigation Global Navigation Satellite System Conference.
Figure legend: This Knowridge.com image is credited to ASPIN Laboratory at UC Riverside.