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Skimming the article quickly, I couldn't understand where the "ground truth" comes from in their algorithm. You have 10-15 phones within 10 km of each other, an

Skimming the article quickly, I couldn't understand where the "ground truth" comes from in their algorithm. You have 10-15 phones within 10 km of each other, and each one pings the GPS satellite to get it's approximate position, sure, but then how do you go from there to the corrected locations?

NullPrefix 3y ago
I would guess it will involve machine learning
toomuchtodo 3y ago
Possibly a combination of https://en.wikipedia.org/wiki/Pseudo-range_multilateration https://en.wikipedia.org/wiki/Pseudo-range_multilateration and ad hoc software driven https://en.wikipedia.org/wiki/Local-area_augmentation_system https://en.wikipedia.org/wiki/Local-area_augmentation_system and https://en.wikipedia.org/wiki/Differential_GPS https://en.wikipedia.org/wiki/Differential_GPS. High level, you're shipping observations from receivers for processing and distribution as local corrections. Every receiver becomes a potential reference station.
- mnw21cam 3y ago
  That doesn't answer the question. Receivers that are typically useful for a local area augmentation system are fixed down, with an accurately-known location. That known location is then the ground truth, and the GPS signals are measured against that and the difference is broadcast to other mobile receivers nearby that use that information to become more accurate. In this scheme, it is unclear how this can be replaced by multiple mobile phones that don't have an accurately-known location.
  - deleted 3y ago
    [deleted]
  - pbmonster 3y ago
    I think you can easily do two primitive things: * Receivers in the metal-reinforced concrete canyons of a big city suffer from multi-path reflections of buildings, leading to bigger uncertainty. You can improve those signals with a (still pretty uncertain) rural reference. This reference typically also sees low angle satellites, and it receives all signals without reflections. * You can improve the uncertainty of some of your references by running analysis on it's position history and compare it with (highly certain) map data. One of your rural signals is moving at 78 mph? High likelihood to be in the left lane on that highway. Moved with 30mph, now suddenly stopped? Waiting at that specific traffic light, probably first car. With crosschecks between enough references, some averaging and smoothing, you can produce a usable reference. Add a couple of absolute references (no reason for the company to not have a fixed antenna in the 50 largest cities each), and you're competing with current commercial reference services. It's also worth noting, that for many of the uncertainties of the GPS signal (satellite clock jitter, atmospheric conditions), you don't need to update your position delta every second. It's already an improvement if you get a good reference update every minute or so.
  - crote 3y ago
    If you have a sufficiently large deployment, there's a pretty good chance that at any point in time some of the phones will be stationary. Filtering out the fast-moving ones is pretty easy, leaving the stationary and slow-moving ones. The whole reason technologies like DGPS work is because receivers in roughly the same location will have roughly the same offset. If you average out the stationary receivers, you will be left with a shared common drift - which can be corrected for. Slow-moving receivers probably don't affect the average too much, and you'd be able to filter them out over time as they'd have movement beyond the shared common drift. Devices will enter the stationary set as they stop moving, and move out of it as they start moving again. Even someone stopped at a traffic light for a few dozen seconds might be able to contribute to it.
flerchin 3y ago
Do the cell towers play a role? They are known locations.
seagor 3y ago
In the academic literature it is called "cooperative positioning" although we use different computational techniques than traditionally tapped by academics. Under the hood we are using techniques from RTK and PPP but in the form of a multi-receiver fueled ensemble optimization to determine the error correction. For testing we use a traditional RTK survey rig to test the results of our error corrections on phones as ground truth. Otherwise it is an exercise in managing and removing error to get as close as possible to ground truth.

NullPrefix 3y ago

I would guess it will involve machine learning

toomuchtodo 3y ago

Possibly a combination of https://en.wikipedia.org/wiki/Pseudo-range_multilateration https://en.wikipedia.org/wiki/Pseudo-range_multilateration and ad hoc software driven https://en.wikipedia.org/wiki/Local-area_augmentation_system https://en.wikipedia.org/wiki/Local-area_augmentation_system and https://en.wikipedia.org/wiki/Differential_GPS https://en.wikipedia.org/wiki/Differential_GPS. High level, you're shipping observations from receivers for processing and distribution as local corrections. Every receiver becomes a potential reference station.

mnw21cam 3y ago
That doesn't answer the question. Receivers that are typically useful for a local area augmentation system are fixed down, with an accurately-known location. That known location is then the ground truth, and the GPS signals are measured against that and the difference is broadcast to other mobile receivers nearby that use that information to become more accurate. In this scheme, it is unclear how this can be replaced by multiple mobile phones that don't have an accurately-known location.
- deleted 3y ago
  [deleted]
- pbmonster 3y ago
  I think you can easily do two primitive things: * Receivers in the metal-reinforced concrete canyons of a big city suffer from multi-path reflections of buildings, leading to bigger uncertainty. You can improve those signals with a (still pretty uncertain) rural reference. This reference typically also sees low angle satellites, and it receives all signals without reflections. * You can improve the uncertainty of some of your references by running analysis on it's position history and compare it with (highly certain) map data. One of your rural signals is moving at 78 mph? High likelihood to be in the left lane on that highway. Moved with 30mph, now suddenly stopped? Waiting at that specific traffic light, probably first car. With crosschecks between enough references, some averaging and smoothing, you can produce a usable reference. Add a couple of absolute references (no reason for the company to not have a fixed antenna in the 50 largest cities each), and you're competing with current commercial reference services. It's also worth noting, that for many of the uncertainties of the GPS signal (satellite clock jitter, atmospheric conditions), you don't need to update your position delta every second. It's already an improvement if you get a good reference update every minute or so.
- crote 3y ago
  If you have a sufficiently large deployment, there's a pretty good chance that at any point in time some of the phones will be stationary. Filtering out the fast-moving ones is pretty easy, leaving the stationary and slow-moving ones. The whole reason technologies like DGPS work is because receivers in roughly the same location will have roughly the same offset. If you average out the stationary receivers, you will be left with a shared common drift - which can be corrected for. Slow-moving receivers probably don't affect the average too much, and you'd be able to filter them out over time as they'd have movement beyond the shared common drift. Devices will enter the stationary set as they stop moving, and move out of it as they start moving again. Even someone stopped at a traffic light for a few dozen seconds might be able to contribute to it.

flerchin 3y ago

Do the cell towers play a role? They are known locations.

seagor 3y ago

In the academic literature it is called "cooperative positioning" although we use different computational techniques than traditionally tapped by academics. Under the hood we are using techniques from RTK and PPP but in the form of a multi-receiver fueled ensemble optimization to determine the error correction. For testing we use a traditional RTK survey rig to test the results of our error corrections on phones as ground truth. Otherwise it is an exercise in managing and removing error to get as close as possible to ground truth.