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I'm currently working on a proof-of-concept to work out the estimated locations (longitude and latitude) of outdoor beacons with unknown locations to begin with.

The solution will use a mobile receiver (phone) to pick up signals within an area covered by large amount of beacons (>500-1000). By walking through the outdoor area, the mobile would gather RSSI signal strengths of surrounding beacons and also its GPS coordinates.

I'm trying to work out if I can tag the GPS locations of surrounding beacons by reversing the 2D trilateration / multi-lateration method as detailed here:

https://www.101computing.net/cell-phone-trilateration-algorithm

From the 2D-trilateration formula, instead of working out the receiver coordinate from the distance of 3 known points, I would want to use 6 known receiver's GPS locations to work out the 3 unknown points (x, y) pairs.

I understand there would be limitations with volatile signal strength / high noise / reflection issues. I would like to know if there are other efficient algorithms to achieve the goal of this proof-of-concept? Any related research work you can guide me to to further develop on?

I would also like to explore if I can obtain additional sensors' readings from the mobile (accelerometer / gyroscope / compass orientation), would that help to solve the problem efficiently?

ocrdu
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LawrenceH
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  • TOF (time of flight) is the current technique being used for indoor location methinks. Newer BLE standards have some location features. There’s also UWB specifically for indoor location. RSSI is way too noisy and inaccurate unless all you require is a fairly coarse location. – Kartman Aug 13 '22 at 03:26
  • Could you clarify the knowns and unknowns? "6 known receiver's GPS locations to work out the 3 unknown points (x, y) pairs" What are the 6 known things and what are the 3 unknowns? How do these relate to the 500+ beacons? – jonathanjo Oct 17 '22 at 13:46

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