Abstract
Recently, the rapid development of mobile devices and communication technologies has dramatically increased the demand for location-based services that provide users with location-oriented information and services. User location in outdoor spaces is measured with high accuracy using GPS. However, because the indoor reception of GPS signals is not smooth, this solution is not viable in indoor spaces. Many on-going studies are exploring new approaches for indoor location measurement. One popular technique involves using the received signal strength indicator (RSSI) values from the Bluetooth Low Energy (BLE) beacons to measure the distance between a mobile device and the beacons and then determining the position of the user in an indoor space by applying a positioning algorithm such as the trilateration method. However, it remains difficult to obtain accurate data because RSSI values are unstable owing to the influence of elements in the surrounding environment such as weather, humidity, physical barriers, and interference from other signals. In this paper, we propose an indoor location tracking system that improves performance by correcting unstable RSSI signals received from BLE beacons. We apply a filter algorithm based on the average filter and the Kalman filter to reduce the error range of results calculated using the RSSI values.
Highlights
With the development of information and communication technology due to the Fourth IndustrialRevolution, the time and space restrictions on access to information are disappearing
To measure a user’s position in an indoor space using a beacon, a plurality of beacons is installed in the area, and the distance between each beacon and a mobile device is measured based on the received signal strength indicator (RSSI) of the beacon
We propose an indoor positioning system that corrects unstable RSSI and improves
Summary
With the development of information and communication technology due to the Fourth Industrial. To measure a user’s position in an indoor space using a beacon, a plurality of beacons is installed in the area, and the distance between each beacon and a mobile device is measured based on the received signal strength indicator (RSSI) of the beacon This method locates the user by applying a positioning algorithm, such as the triangulation method, under the assumption that the calculated distance between each beacon and mobile device is accurate. We propose an improved indoor positioning system that measures user’s position in indoor space For this purpose, we applied the appropriate filter algorithm based on the extended Kalman filter (EKF) to the unstable RSSI value to reduce the error range of the computed distance.
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