AI News, Cheap Centimeter-Precision GPS For Cars, Drones, Virtual Reality

Cheap Centimeter-Precision GPS For Cars, Drones, Virtual Reality

The GPS navigation system in your mobile phone can get you to the airport or the closest coffee shop.

Such centimeter precision could letdrones deliver packages to your porch, autonomous vehicles navigate safely, and be used inprecision farming.

“If we want to see autonomous or even semi-autonomous vehicles go mainstream, we need a cheap way to provide centimeter or decimeter-scale accuracy.” So he and his colleagues decided to experiment with the cheap, low-quality antennas found in smartphones.

Unlike the fancy expensive ones, these antennas, which Humphreysdescribes as lookinglike “smashed paper clips,” are especially susceptible to crucial errors that can reduce GPS positioning accuracy.

One such error, called multipath error, is due to RF signals from GPS satellites that reach the antenna after bouncing off the ground or objects like trees and buildings.

The UT Austin team has created software over the past six years that uses sophisticated signal processing techniques to process the data from a smartphone GPS antenna and reduce the effect of those errors.

New Centimeter-Accurate GPS System Could Transform Virtual Reality and Mobile Devices

AUSTIN, Texas — Researchers in the Cockrell School of Engineering at The University of Texas at Austin have developed a centimeter-accurate GPS-based positioning system that could revolutionize geolocation on virtual reality headsets, cellphones and other technologies, making global positioning and orientation far more precise than what is currently available on a mobile device.

The researchers’ new system could allow unmanned aerial vehicles to deliver packages to a specific spot on a consumer’s back porch, enable collision avoidance technologies on cars and allow virtual reality (VR) headsets to be used outdoors.

The researchers’ new centimeter-accurate GPS coupled with a smartphone camera could be used to quickly build a globally referenced 3-D map of one’s surroundings that would greatly expand the radius of a VR game.

“To be able to do this type of outdoor, multiplayer virtual reality game, you need highly accurate position and orientation that is tied to a global reference frame.” Humphreys and his team in the Radionavigation Lab have built a low-cost system that reduces location errors from the size of a large car to the size of a nickel — a more than 100 times increase in accuracy.

The breakthrough by Humphreys and his team is a powerful and sensitive software-defined GPS receiver that can extract centimeter accuracies from the inexpensive antennas found in mobile devices — such precise measurements were not previously possible.

GPS chaos: How a $30 box can jam your life

Signals from GPS satellites now help you to call your mother, power your home, and even land your plane – but a cheap plastic box can jam it all IT WAS just after midday in San Diego, California, when the disruption started.

In the tower at the airport, air-traffic controllers peered at their monitors only to find that their system for tracking incoming planes was malfunctioning.

Chaos threatened in the busy harbour, too, after the traffic-management system used for guiding boats failed.

On the streets, people reaching for their cellphones found they had no signal and bank customers trying to withdraw cash from local ATMs were refused.

Some are worried that we are now leaning too heavily on a technology that can all too easily fail – and it doesn’t need a freak navy training exercise to cause havoc.

Their biggest concern is a GPS jammer – a plastic device that can sit on car dashboards.

Their increasing use has already caused problems at airports and blocked cellphone coverage in several cities.

No surprise, then, that researchers across the world are scrambling to find ways to prevent disastrous GPS outages happening.

The dominant provider is still the US military’s NavStar network, with at least 24 satellites operating at any given time, positioned so that you can always see four of them from anywhere on the planet’s surface.

Each satellite continually broadcasts its location and the time as measured by its on-board atomic clock.

So he used a simple jamming device that overwhelmed the GPS signal by broadcasting noise on the same frequency as the satellites.

The ship’s navigation backup – its gyrocompass – crashed, because it uses GPS to provide corrections.

Since they can block devices that record a vehicle’s movements, they’re popular with truck drivers who don’t want an electronic spy in their cabs.

– which subtly trick GPS receivers into giving false readings – may make the problem even worse (see “Faking it”).

It is estimated that more that a billion GPS receivers are now in operation, he says, and more than 90 per cent use the signals only for the accurate time provided by the satellites.

Towers must synchronise with each other to pass calls to other towers as you move – a GPS time signal offers a cheap and accurate way to do this.

Energy suppliers use GPS time to keep alternating current from various power plants in phase across the grid.

Yet in 2006, a temporary GPS outage due to sunspot activity meant that energy companies were not able to see where the power was going, which resulted in false billing.

The problem for western authorities is that most sellers are in east Asia and laws tend only to cover the use of a jammer, not its ownership.

new version, eLORAN, uses more reliable transmitters and features improved caesium atomic clocks.

With software modifications, it is accurate to about 10 metres, as well as providing a time signal of similar accuracy to GPS.

And eventually you’ll be able to navigate without any external signals, thanks to devices called “inertial measurement units”, which track your movements from a known start point.

Using this information, plus time, the acceleration is converted into speed and distance to reveal relative location.

Yet the US Defense Advanced Research Projects Agency plans to improve performance with a microchip-sized atomic clock and an equally diminutive, accurate acceleration sensor.

So next time you lose your cellphone signal, blame the little black box on a car dashboard a few kilometres away.

Accuracy in the Palm of Your Hand

Humphreys, the University of Texas at Austin The smartphone antenna’s poor multipath suppression and irregular gain pattern result in large time-correlated phase errors that significantly increase the time to integer ambiguity resolution as compared to even a low-quality stand-alone patch antenna.

The latest clock, orbit, and atmospheric models have improved ranging accuracy to a meter or so, leaving receiver-dependent multipath and front-end-noise-induced variations as the dominant sources of error in current consumer devices.

Currently, the primary impediment to performing CDGNSS positioning on smartphones lies not in the commodity GNSS chipset, which actually outperforms survey-grade chipsets in some respects, but in the antenna, whose chief failing is its poor multipath suppression.

Multipath, caused by direct signals reflecting off the ground and nearby objects, induces centimeter-level phase measurement errors, which, for static receivers, have decorrelation times of hundreds of seconds.

The large size and strong time correlation of these errors significantly increases the initialization period — the so-called time-to-ambiguity-resolution (TAR) — of GNSS receivers employing CDGNSS to obtain centimeter-level positioning accuracy.

In this so-called short baseline regime, the differential ionospheric delay between the reference and mobile receivers becomes insignificant, obviating differential delay estimation via multi-frequency measurements.

Test Architecture We used the test architecture shown in Figure 1 to collect data from a smartphone-grade antenna and higher quality antennas, process these data through a software-defined GNSS receiver, and compute a CDGNSS solution on the basis of the carrier phase measurements output by the GNSS receiver.

The clock attached to the external front-end was an oven-controlled crystal oscillator (OCXO), which has much greater stability than the low-cost oscillators used to drive GNSS signal sampling within smartphones.

However, it was found that reliable cycle-slip-free GNSS carrier tracking only required a 40-ms coherent integration (pre-detection) interval, which is within the coherence time of a low-cost temperature-compensated crystal oscillator (TCXO) at the GPS L1 frequency.

CDGNSS Processing The CDGNSS filter described in this section ingests double-differenced carrier phase measurements output from GRID and processes them to produce (1) the centimeter-accurate trajectory estimate of the mobile antenna, (2) a time history of phase residuals, (3) carrier phase integer ambiguity estimates, (4) theoretical integer ambiguity resolution success bounds, and (5) empirical integer ambiguity resolution success rates.

The filter’s state has a real-valued component xk that models the mobile antenna’s relative center of motion, its instantaneous offset from this center of motion, and its velocity at each time epoch k: .

The filter ingests measurement vectors yk for k = 1, …, K, each populated with a single epoch of double-differenced carrier phase measurements   for i = 1, 2, .

The filter’s measurement model relates yk to the real- and integer-valued state components through the following linearized GNSS carrier phase measurement model:  (3) where rxk is a vector of double-differenced modeled ranges based on the filter’s real-valued state prior , Hxk and Hn are the measurement sensitivity matrices for the real- and integer-valued state components, and vk is the double-differenced measurement noise vector, all at time k.

After processing data through the CDGNSS filter, the filter outputs, in addition to a time history of centimeter-accurate position estimates, a time history of phase residuals , which can be thought of as departures of each double-differenced phase measurement from phase alignment at the phase center of the antenna.

These residuals can be modeled as   (4)where rxk is now based on the filter’s real-valued state estimate    at time k and  represents the filter’s estimate of the integer ambiguities at time K.

The loss numbers in the far-right column represent the average loss in gain relative to a survey-grade antenna, where the average is taken over elevation angles above 15 degrees.

Survey-grade antennas, whose properties are described in the first row of Table 1, have a uniform quasi-hemispherical gain pattern, right-hand circular polarization, a stable phase center, and a low axial ratio.

The rightmost histogram, in green, shows that the decrease in carrier to noise ratio as compared to a survey-grade antenna is on average 11 dB, such that the smartphone-grade antenna only captures approximately 8 percent of the signal power as compared its survey-grade counterpart.

Shown in Figures 3, 4, and 5 are 2,000-second segments of double-differenced phase residual time histories for data collected from a survey-grade, a low-quality patch, and a smartphone-grade antenna, respectively.

These outliers, one of which persists for over 1,000 seconds, are likely caused by either large and irregular azimuth- and elevation-dependent antenna phase center variations or a combination of poor antenna gain in the direction of the non-reference satellite coupled with ample gain in the direction of a multipath signal such that the multipath signal is received with more power than the direct-path signal.

however, cases do arise in which the residuals are considerably worse due to a combination of poor antenna gain in the direction of the non-reference satellite, coupled with ample gain in the direction of a multipath signal.

The key to this technique’s effectiveness is that, whereas multipath-induced phase measurement errors are typically time-correlated on the order of hundreds of seconds for a static receiving antenna, their spatial correlation is on the order of one wavelength, or approximately 19 centimeters at the GPS L1 frequency.

Put another way, autocorrelation time of the phase residuals decreases from hundreds of seconds when the antenna is static, as shown in Figure 11, to less than a second when the antenna is moved even slowly (a few centimeters per second), as shown in Figure 12.

The shorter phase error decorrelation time resulting from random antenna motion effectively increases the information content per unit time that each double-differenced phase measurement provides to the CDGNSS filter, thus decreasing the time to ambiguity resolution.

While it is true that the phase measurement errors decorrelate much faster when the antenna is moving — increasing the per-epoch information provided to the filter — it is also the case that the filter can no longer employ a hard motion constraint.

For the high-quality antennas, the increased information per epoch due to faster phase error decorrelation is completely counteracted by a loss in information per epoch due to uncertainty (lack of constraint) in the motion model.

Also, for the high-quality antennas, multipath in the reference antenna’s phase measurements is not insignificant compared to multipath in the mobile antenna, and this reference multipath exhibits the usual 100–200 second correlation time for a static antenna.

An empirical analysis revealed that the extremely poor multipath suppression of these antennas is the primary impediment to fast resolution of the integer ambiguities that arise in the carrier phase differential processing used to obtain centimeter accuracy.

Future work will study the effectiveness of combining antenna motion with a motion trajectory estimate derived from non-GNSS smartphone sensors to further reduce the integer ambiguity resolution time.

This technique, which is a type of synthetic aperture processing applied to the double-differenced GNSS phase measurements, effectively points antenna gain enhancements in the direction of the overhead GNSS satellites, thereby suppressing multipath arriving from other directions.

Todd Humphreys: How to fool a GPS

Todd Humphreys forecasts the near-future of geolocation when millimeter-accurate GPS "dots" will enable you to find pin-point locations, index-search your physical possessions ... or to track...

How GPS Works

The Global Positioning System, or GPS, is pretty amazing and chances are, it's playing a much greater role in your life than you realize. Anthony explains how GPS works and tells you about...

GPS Carrier Phase Ranging - SixtySec

Visit for more videos on this topic

TEDxAustin - Todd Humphreys

What if you could use GPS technology to find your misplaced keys? How about if you could use that same technology to lie about where you were in the world or misdirect cruise ships? Todd Humphreys...

Mod-01 Lec-3 GPS Positioning Methods

Modern Surveying Techniques by Prof. S.K. Ghosh,Department of Civil Engineering,IIT Roorkee.For more details on NPTEL visit

GPS Carrier Phase Video

This video blog on GPS Carrier Phase, is broken down into 6 segments and is intended to give an overview of the subject and includes additional reading resources for a more in-depth study if...

GPS Carrier Phase Videoblog

This video blog on GPS Carrier Phase, is broken down into 6 segments and is intended to give an overview of the subject and includes additional reading resources for a more in-depth study if...

Improve gps accuracy on android

much more simple way to improve the accuracy of android, but if all else failed, try this way.