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iBeacon Indoor Positioning Method Combined with Real-Time Anomaly Rate to Determine Weight Matrix
Yu Guo1, Jiazhu Zheng1, Weizhu Zhu1
1Civil Engineering College, Nanjing Forestry University, Nanjing 210037, China.
This study introduces an improved indoor positioning system using iBeacon technology, anomaly detection, and a weighted Levenberg-Marquardt algorithm. The method enhances accuracy by filtering signal errors, achieving precise location determination.
Area of Science:
- Engineering
- Computer Science
- Signal Processing
Background:
- Accurate indoor positioning remains a challenge.
- Existing methods often struggle with signal interference and noise.
Purpose of the Study:
- To develop a robust indoor positioning method using iBeacon technology.
- To enhance positioning accuracy by integrating anomaly detection with a weighted algorithm.
Main Methods:
- Utilized iBeacon technology for data collection.
- Employed the isolation forest algorithm for anomaly detection in Received Signal Strength Indicator (RSSI) data.
- Applied a weighted Levenberg-Marquardt (LM) algorithm with a dynamically generated weight matrix.
Main Results:
- Achieved an average positioning error of 1.540 m and an RMSE of 1.748 m.
- 85.71% of positioning errors were below 3 m.
- Demonstrated significant RMSE reduction compared to three other methods (38.69%, 36.60%, 29.52% lower).
Conclusions:
- The proposed iBeacon-based method effectively improves indoor positioning precision.
- The integration of anomaly detection and weighted LM algorithm offers practical advantages for real-world applications.
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