For those of you curious how it works, it's a parasitic radar system using the wireless APs of you/your neighbors to triangulate it's own position.
https://github.com/schollz/find/blob/master/FAQ.md#how-does-...
https://github.com/schollz/find/blob/master/FAQ.md#how-does-...
That's an accurate description, though I'd avoid the negative terminology. The system does derive its data from nearby WiFi routers, but it is in no way exploitative, like real parasites. WiFi scanning is something all smartphones do all the time, so this system merely tries harnessing it into something useful at the same time.
As for the technical side - this system uses a Naive Bayes estimator for the signal levels from mac addresses, as well as the presence of mac addresses it sees during scans. Before it can be used it needs to "learn" by visiting all the locations you want to identify. After learning it "tracks" by classifying the location used the Naive bayes estimators.
> How do you compare the current fingerprint of a user with the one stored in database?
The fingerprints are used to generate priors. Basically a prior is the probability for encountering router X with signal strength Y in room Z. These are real probabilities. When I classify a location, it uses these probabilities and Bayes law to determine new "Bayes probability."
> How do you determine the probability for classification?
You are now referring to the probability on the dashboard (the webpage) I assume. Thanks for the question. This is inherently confusing, since really it is NOT a real probability so its a bit of a misnomer. Lets call them my "probability estimate." Technically speaking, the Bayes probabilities (real) are taken and normalized to the standard normal distribution. Then I determine the "probability estimates" by the proportion of the exponent of the standard normal normalized Bayes probabilities.
Would you like to offer up another term for something that performs ranging with radio?