FIND is an open source indoor locating system for home automation, indoor local positioning and passive tracking. It uses your smartphone or laptop to pinpoint your position in your home or office with a location precision of below 10 sq ft.
FIND uses scanning of WiFi and Bluetooth:
FIND compiles these different signals can be compiled into a fingerprint which can be used to uniquely classify the current location of that device
The main insight is that along with the expected difference in the RSSI attenuation there is a considerable difference in the BLE signal variation at all transmission power levels with respect to distance. The variation increases and the localisation accuracy decreases from high to low transmission power levels:
Another observation is that outliers in the data tend to affect the localisation accuracy. Applying filters to the data, they achieved a location accuracy of 2.2 meters with a precision of 95%.
One comment we have is that the researchers didn’t try different beacons. As we mentioned in 2016, the RSSI stability also varies across different beacon models.
Motorola MOTOTRBO range two-way Radios can be used with the Motorola-supplied TRBOnet PLUS (pdf) control room software to show the location of workers with digital radios on maps and plans. The radios contain both GPS and iBeacon detection to allow locating indoors.
There are three places where iBeacons need to be set up in TRBOnet:
In the GPS profile:
Placing beacon on the map:
Are you an established 2-way radio company? Contact us for advice on which beacons we have supplied for use with TRBONet.
There’s a video at YouTube on the installation of Raspberry Pi based beacon detectors in a cow shed to detect the position of cows.
Beacon on a cow
Beacons can, in fact, do a lot more than just determine location. For example, it’s possible to track extra things such as temperature, humidity and unexpected movement. In the cow shed case, hall effect beacon sensors can be put on gates to alert when gates are open/closed when they shouldn’t be. The location data can be used to provide geofencing to alert when things, people or animals enter or leave specific areas.
They evaluated RSSI and indoor positioning trilateration algorithms in order to determine location accuracy. After lots of experimentation and mathematics, they calculated the average error to be 1.09m for 1–9m and 1.75m for 1-20m and after trilateration an average error 2.45m was achieved.
The conclusions give some hints how better results might be achieved. For example, correlating the RSSI with accelerometer, gyroscope and other sensors. Other strategies might be to excluding areas where an object
cannot move, or filtering out situations where objects move but accelerometer measurements don’t match.
IndoorAtlas has a new free, openly accessible 2016 Indoor Positioning Research Report that has some insights regarding indoor positioning and beacons. However, while reading the report you should know IndoorAtlas is trying to position their geomagnetic indoor positioning solution.
The report says the main concerns for implementing beacon indoor positioning systems are scalability (40%) and expense (38%).