I had an internship project which was a simpler cousin of this, where I needed to determine the approximate location of a WiFi-enabled device, based off of rece

I had an internship project which was a simpler cousin of this, where I needed to determine the approximate location of a WiFi-enabled device, based off of received signal strengths from a several access points. Normally this would be trivial, but this demo was meant to simulate an environment highly reflective to 2.4GHz RF. So while it took only a day or 2 to demonstrate relatively poor performance using simple triangulation (actually, trilateration is the better word here), I spent several weeks collecting data and putting it through a support vector machine. With a simple moving average filter on top of that SVM, around 98-99% accuracy was pretty easily achievable in classification (I believe my prediction classes were 2 x # of rooms, so quite coarse but good enough for the task). The main advantage over a physics-based modeling approach - which with enough information, could surely have reached practically 100% accuracy - is that the SVM didn't rely on knowing anything about the location of the access points, or the geometry of the space. The signal strength training data was to be available for free as a biproduct of another device, so this solution had very low cost in the form of manual effort/precise measurement, both of which would have dwarfed a few weeks of intern time.