> In the end it's triangulation of a signal. No need for ML after all. Triangulation works accurately in open space, however this use case is inside a home wit

> In the end it's triangulation of a signal. No need for ML after all. Triangulation works accurately in open space, however this use case is inside a home with signals being blocked, bouncing, or attenuated by walls, mirrors, appliances, etc. On top of that, the goal is to use as few BLE rx'ers as possible (meaning not one per room). Given those constraints, a trained ML model actually does make sense.