Automatic screen locking when you walk away probably sounds straightforward. But Bluetooth proximity detection has a fundamental challenge: signal noise can trigger false locks when you haven't actually moved.
The problem is Bluetooth signal noise. Your phone's signal strength jumps around constantly. One moment it reads -65 dBm, the next it's -90 dBm, then back to -64 dBm. Nothing changed except maybe you shifted in your chair or someone started the microwave.
Simple thresholds can't handle this kind of noise. You need something smarter that can tell the difference between real movement and random interference. That's where signal processing comes in. Kalman filter was originally developed for spacecraft navigation, where you also have to make decisions from noisy sensor data.
How It Works
Bluetooth wasn't designed for proximity detection. It was made for transferring data. The signal strength can change dramatically even when you don't move. WiFi networks interfere with it. Microwaves mess with it. Even rotating your phone changes the signal. Simple detection can't tell if you actually left or if someone just started the microwave.
Kalman filter solves this problem by accounting for uncertainty. Instead of using any single measurement directly, the algorithm maintains a mathematical model of the underlying signal. Rudolf Kálmán developed this approach in the late 1950s for spacecraft navigation. NASA used it for the Apollo program, and now it's in GPS systems, airplane autopilots, and economic models.
Think of it like driving when your GPS briefly shows you in the middle of a lake. You don't slam the brakes and call for a boat. Your brain uses context to filter out the bad data. You know you were just on a road and cars don't teleport.
Moving Averages vs Kalman Filtering
A common approach to noise is moving averages. This means taking the average of the last 5-10 signal readings. This seems logical: if most recent readings are strong, you're probably still there.
But moving averages have limitations for proximity detection. It treats all recent measurements equally regardless of timing, which means that it doesn't capture whether a sudden change just occurred or whether values change gradually over time. Basically it can't distinguish real movement from interference.
A moving average sees readings like [-65, -63, -90, -64, -66] and concludes you're at -70 dBm. That -90 spike gets equal weight with stable readings. Percentile approaches like median (-64 dBm) can handle outliers better but still can't distinguish patterns.
Kalman filter models the underlying signal as a time-evolving process. It tracks both position (current RSSI) and velocity (rate of change). When it sees [-65, -63, -90, -64, -66], it recognizes the -90 breaks the stable trend and downweights it. More importantly, it can distinguish gradual decline over 30 seconds (walking away) from isolated spikes (interference).
But the math alone isn't enough for reliable proximity detection.
Extra Tricks
The basic Kalman filter math handles most of the noise, but proximity detection needs a few extra tricks.
First, the filter watches how fast your signal changes. A steady decline means you're walking away. A sudden drop to zero with no pattern? Probably interference.
When the app detects a potential false alarm, it waits longer if your signal was recently strong. If your signal is usually weak, it doesn't wait as long.
When it's unsure what's happening, it checks more often at first, then less often. It helps finding your device quickly if it comes back but saves battery if you really went away.
It's mostly about edge cases. The system ignores invalid readings. Zero readings are treated specially because they often mean temporary communication problems, not that you left. If Bluetooth stops working completely, there's a recovery system.
So does all this complexity actually help?
Does It Work?
The filtering approach reduces false triggers from signal noise. You can shift in your chair, lean back, or turn around without triggering a lock. When you actually step away from your desk, it detects that and locks your Mac.
It also uses less battery than constantly scanning. The adaptive grace periods learn from your signal loss patterns to reduce false alarms.
In Practice
In shared workspaces with lots of WiFi networks and other signals, filtering helps tell when you're actually moving versus just interference. At home, interference from other devices is less likely to cause false locks. In cafes, libraries or other public spaces with lots of different signals around and obstructions, it handles interference better.
Bluetooth proximity detection has inherent limitations compared to dedicated proximity sensors or UWB technology. But signal processing can improve reliability within those constraints.
Why It Matters for Security
False positives create security trade-offs. Too many false alarms can lead to disabling the feature, using overly permissive thresholds, or longer timeout delays that leave machines unlocked when users actually leave.
Better filtering enables more aggressive security settings by reducing false triggers while maintaining responsiveness to actual movement.
The Bottom Line
The filtering approach reduces false triggers and catches most edge cases. It's not perfect. No Bluetooth-based system is. But it handles the majority of real-world situations. Just don't make it your only security measure.