University of California- Riverside researchers have developed a new and more efficient way for Global Positioning Systems (GPS) to process data so that they can enhance location accuracy (quite literally) down to the last centimeter.
This research was led by Professor Jay Farrell, chair of electrical and computer engineering in the university’s Bourn’s College of Engineering. Their study has been published in IEEE’s Transactions on Control Systems Technology.
Ultimately, their approach involves reformulating a series of equations that are used to determine a GPS receiver’s position, resulting in reduced computational effort being required to attain centimeter accuracy.
The new and improved GPS system will be implemented in autonomous vehicles, aviation and naval navigation systems. Since GPS is available on mobile phones and wearable technologies, it will give users more accurate data without increasing the demand for processing power.
The Global Positioning System was first conceptualized in the 1960s. It is a space-based navigation system that enables a receiver to compute its location and velocity by measuring the time it takes for the radio signals from four or more overhead satellites to reach the respective satellites. Thus, the standard GPS can accurately measure approximately 10 meters.
Another type of GPS, differential GPS, has improved accuracy to about one meter. It does this by enhancing the system using a network of fixed ground-based reference stations. Even though differential GPS is better than the standard GPS, it is still not accurate enough to be used safely on autonomous cars and other related applications.
“To fulfill both the automation and safety needs of driverless cars, some applications need to know not only which lane a car is in, but also where it is in that lane—and need to know it continuously at high rates and high bandwidth for the duration of the trip,” said Farrell, whose research focuses on developing advanced navigation and control methods for autonomous vehicles.
Farrell said these requirements can be achieved by combining GPS measurements with data from an inertial measurement unit (IMU) through an internal navigation system (INS). The GPS provides data to achieve high accuracy, while the IMU provides data to achieve high sample rates and high bandwidth continuously.
“Achieving this level of accuracy with computational loads that are suitable for real-time applications on low-power processors will not only advance the capabilities of highly specialized navigation systems, like those used in driverless cars and precision agriculture, but it will also improve location services accessed through mobile phones and other personal devices, without increasing their cost,” Farrell said.