An Improved Indoor Positioning Accuracy Using Filtered RSSI and Beacon Weight

Increasing the location accuracy of the objects in the Indoor Positioning System (IPS) has grasped great attention lately. With the recent developments in the fields of smartphones and mobile beacons, the accuracy of the IPS has achieved accuracy with less than a meter of accuracy. In this paper, we proposed and developed a Filtered RSSI and Beacon Weight Approach (FRBW) based on improved Received Signal Strength Indicator using Kalman filter. The developed algorithm takes the distance and the smoothed RSSI values between beacon nodes into consideration. We employ Kalman filtering on the RSSI measurements of the Beacon signals before applying FRBW algorithm. The developed FRBW algorithm was applied and validated in indoor environment using Bluetooth Low Energy beacons and the experimental results achieves accuracy of a few centimeters localization using cost-effective and easy to deploy beacons.

Automated summary

The study presents a Filtered RSSI and Beacon Weight Approach (FRBW) that utilizes Kalman filtering for indoor positioning with improved accuracy. By considering the distance and smoothed RSSI values between beacon nodes, the algorithm achieves localization accuracy within a few centimeters using cost-effective Bluetooth Low Energy beacons.