Proximity Shortcuts

The Problem

Wireless signals are inherently unstable. Your device might measure -65 dBm, then suddenly drop to -90 dBm, then bounce back, all while you're sitting still. Environmental factors create constant noise.

Why Simple Averaging Fails

Basic approaches weight every reading equally, making them unable to distinguish random interference from genuine distance changes as you walk away.

The Solution: Signal Processing

ProximityShortcuts uses a prediction model that estimates both your current distance and how it's changing over time. Each new signal measurement is weighted against this prediction: measurements that align with the expected trend are trusted more, while unexpected spikes are automatically dampened. This allows the filter to follow genuine movement smoothly while filtering out random interference.

This filtering technique was developed by Rudolf E. Kálmán in 1960 and became crucial for the Apollo program's navigation systems, where reliable decisions from uncertain sensor readings were critical. The same Kalman filter mathematics now powers smartphone GPS, autonomous vehicles, and financial forecasting.

The algorithm's computational efficiency made it practical for the Apollo program despite the limited computing power of the 1960s. That same efficiency remains its strength today: ProximityShortcuts performs these calculations continuously in the background using negligible CPU and energy resources, making proximity detection practical and reliable.