We recently highlighted an article on Beacon Trajectory Smoothing. Faheem Zafari, Ioannis Papapanagiotou, Michael Devetsikiotis and Thomas Hacker have a new paper on An iBeacon based Proximity and Indoor Localization System (pdf) that also uses filtering.
They use a Server-Side Running Average (SRA) and Server-Side Kalman Filter (SKF) to improve the proximity detection accuracy compared to Apple’s immediate, near and far indicators.
The researchers found:
The current (Apple) approach achieved a proximity detection accuracy of 65.83% and 67.5% in environment 1 and environment 2 respectively. SRA achieved 92.5% and 96.6% proximity detection accuracy which is 26.7% and 29.1% improvement over the current approach in environment 1 and 2 respectively
What’s interesting here is that the researchers have quantified the accuracy of Apple’s implementation in two scenarios. The accuracy isn’t that good and as the researchers have shown, can be improved upon significantly.