Category: RSSI

Research Paper on Using Bluetooth for Indoor Locating

There’s a paper by Mariusz Kaczmarek, Jacek Ruminski and Adam Bujnowski of Gdansk University of Technology on the Accuracy analysis of the RSSI BLE SensorTag signal for indoor localization purposes (pdf).

They studied the radio signal from multiple Texas Instruments SensorTag CC2650 devices in order to determine if it could be used to determine location.

They concluded:

“Given the large number of factors governing the received RSSI, calibration is unlikely to be able to compensate for all of
them, leading us to conclude that there is an inherent limit to the accuracy of a BLE positioning system especially when multiple devices are used.”

They suggest:

…that instead of using a single RSSI measurement to estimate distance, try using the average or median value of N measurements collected on the same spot (at least N>20) so that you can reduce the effect of small scale fading.

Improving on Beacon Immediate, Near and Far

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.

Beacon Trajectory Smoothing

The problem with using RSSI for detecting location is that raw data contains lots of noise. Also, this noise becomes more prevalent in the viewed data when location samples are taken less often. There’s a useful new article at InfoQ on Processing Streaming Human Trajectories with WSO2 CEP.

The idea uses Kalman filtering to smooth noisy human trajectories.

Before and after filtering

This method is particularly useful for large realtime IoT rollouts because it uses a very small memory resource, is very fast and the calculation is recursive, so new values can be processed as they arrive.

Beacon Location Accuracy

There’s some recent new research on ‘Analysis of Object Location Accuracy for iBeacon Technology based on the RSSI Path Loss Model and Fingerprint Map’ by Damian Grzechca, Piotr Pelczar, Łukasz Chruszczyk.

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.

Crowd Analysis Using Beacons

With so many uses of beacons centred around notifications to users, it’s interesting to see Queen Mary University of London doing something different. Research by Kleomenis Katevas, Laurissa Tokarchuk, Hamed Haddadi and Richard G. Clegg of the Department of Computing of Imperial College looks into detecting group (crowd) formations using iBeacon (pdf).

They used beacon RSSI and phone motion together with algorithms based on graph theory to predict interactions inside the crowd. They verified their finding using using video footage as ground truth.

distanceestimationmodels

The paper has some particularly interesting observations from testing RSSI in an EMC screened anechoic chamber and also has some information on distance estimation models.