Abstract
This paper proposes an indoor positioning method based on iBeacon technology that combines anomaly detection and a weighted Levenberg-Marquadt (LM) algorithm. The proposed solution uses the isolation forest algorithm for anomaly detection on the collected Received Signal Strength Indicator (RSSI) data from different iBeacon base stations, and calculates the anomaly rate of each signal source while eliminating abnormal signals. Then, a weight matrix is set by using each anomaly ratio and the RSSI value after eliminating the abnormal signal. Finally, the constructed weight matrix and the weighted LM algorithm are combined to solve the positioning coordinates. An Android smartphone was used to verify the positioning method proposed in this paper in an indoor scene. This experimental scenario revealed an average positioning error of 1.540 m and a root mean square error (RMSE) of 1.748 m. A large majority (85.71%) of the positioning point errors were less than 3 m. Furthermore, the RMSE of the method proposed in this paper was, respectively, 38.69%, 36.60%, and 29.52% lower than the RMSE of three other methods used for comparison. The experimental results show that the iBeacon-based indoor positioning method proposed in this paper can improve the precision of indoor positioning and has strong practicability.
Highlights
Nowadays, Global Navigation Satellite System (GNSS) technology, navigation, and positioning services have become an indispensable service in people’s lives, especially for travel-related services
3 of to first collect the Received Signal Strength Indicator (RSSI) values of all iBeacon base stations deployed in the indoor scene, use the isolation forest algorithm to perform anomaly detection on the RSSI values of and use the isolation forest algorithm to perform anomaly detection on the RSSI
Rate of the signal broadcast by each iBeacon base station is calculated, and the average the anomaly rate of the signal broadcast by each iBeacon base station is calculated, and value of each group of signals, after removing the abnormal RSSI signal, is adopted as the average value of each group of signals, after removing the abnormal RSSI signal, is the final RSSI at the current point
Summary
Global Navigation Satellite System (GNSS) technology, navigation, and positioning services have become an indispensable service in people’s lives, especially for travel-related services. Such location services cannot be provided indoors, due to difficulties in receiving signals from GNSS satellites [1]. Cheap, stable, and high-precision positioning and navigation in indoor contexts where satellite signals are missing has become an urgent problem to be solved. At present, positioning and navigation services for indoor uses mainly rely on technologies such as Wireless Local. Indoor positioning and navigation services based on this technology have already been commercialized in many large shopping malls, parking lots, and other such venues [6]
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