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Comment by coupdejarnac | Hacker News Reader

Yeah, that makes sense. But that's not going to be very accurate. That will be accurate at the tuple space, but not down to the nearest centimeter in X Y coordinates or anything like that.

Plus, if there is any mass between the marker and the tracked device, that will throw off readings. RSSI measurements are really only good for open air measurements or if you have characterized all the materials the rf passes through.

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> That will be accurate at the tuple space, but not down to the nearest centimeter

Yes, you're right. Sorry, I didn't mean to imply centimeter-level precision anywhere. I said "high precision" because I compare it to GPS and not RTT (which was not common when I started this project). Unlike FIND, any nearest-centimeter precision requires specialized hardware (to be available on Android P in the future! [1]).

The FIND system fills a niche where you might need room-level or sub-room level precision without having to install anything except an app on your phone or computer.

> any mass between the marker and the tracked device will throw off readings

Its true that individual readings will be affected by varying obstacles (doors/people), and this will throw off location classification if you have very few signal generating devices in the vicinity. However, generally places will see Bluetooth/WiFi coming from their neighbors in all directions so it would be hard to attenuate all signals simultaneously. The machine learning is pretty robust too, so if one of the readings get thrown off because there are still several others that can compensate.

[1]: https://developer.android.com/preview/features.html#rtt

  nearest-centimeter precision requires specialized hardware
  (to be available on Android P in the future! [1]).

Sadly, that link says "The result is typically accurate within 1 to 2 meters" so it's nearest meter rather than nearest centimetre.

Yeah, BLE beacons aren't particularly accurate for an IPS. I worked on this problem a few years ago at FB, for the purposes of helping employees find themselves + meeting rooms on a mobile app. We really only needed 2-3 meter accuracy, so people could figure out approximately where they are. BLE signals are crazy noisy, so you need to tack on a lot of filtering (kalman filter, etc) and it really helps if you have multiple data sources (wifi signals, gyroscope, RF, etc). We could also kinda cheat by adding a track to the center of each hallway on the map, then magnet users towards the track.

That sounds very similar to this! Did FB ever open-source it?

Unfortunately no. Last I worked on it it was pretty heavily integrated with their mobile maps application (https://code.facebook.com/posts/504406169737284/instrumentin...) and there wasn't any plan on open sourcing.

There was a lot of interest in BLE beacons a couple of years back for retail displays and the like. But as you say beacons don’t really work all that well, people had to install apps, etc. and the whole space seems to have sort of fizzled. The wireless beacons also don’t really have good enough battery life for a lot of applications. I played around with some of this stuff but it wasn’t good enough for anything practical that I was interested in.

I wish there was a way to do better phone tracking but it’s hard to come up with a system that would be fast and reliable if you left your phone down somewhere while not driving you crazy with false alarms on a day to day basis.

>lus, if there is any mass between the marker and the tracked device

there are no markers so there is no attenuation happening in that way.

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