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Indoor Positioning Algorithm Based on the Improved RSSI Distance Model.

Guoquan Li1,2, Enxu Geng3,4, Zhouyang Ye5

  • 1School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. ligq@cqupt.edu.cn.

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Summary
This summary is machine-generated.

Indoor positioning struggles with Global Navigation Satellite System (GNSS) inaccuracy. This study introduces a real-time Bluetooth Received Signal Strength Indication (RSSI) correction method using gateways and a PSO-BPNN model for improved accuracy.

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BluetoothKalman filterRSSI distance modelindoor positioning

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Area of Science:

  • * Indoor positioning and navigation systems.
  • * Wireless communication technologies.
  • * Machine learning applications in localization.

Background:

  • * Global Navigation Satellite System (GNSS) is ineffective for precise indoor positioning.
  • * Bluetooth beacon technology is a common indoor positioning method.
  • * Time-varying Received Signal Strength Indication (RSSI) in Bluetooth leads to significant ranging errors with traditional fixed path loss models.

Purpose of the Study:

  • * To develop an efficient indoor positioning technology to overcome GNSS limitations.
  • * To improve the accuracy of Bluetooth beacon positioning by addressing RSSI fluctuations.
  • * To introduce a novel RSSI real-time correction method and an optimized RSSI distance model.

Main Methods:

  • * Implemented a Bluetooth gateway-based RSSI real-time correction method to detect and upload RSSI fluctuations.
  • * Developed a system where terminals adjust collected RSSIs using cloud-stored fluctuation data.
  • * Utilized a particle swarm optimization-optimized backpropagation neural network (PSO-BPNN) for RSSI distance modeling.

Main Results:

  • * The proposed real-time RSSI correction method significantly reduces ranging errors caused by signal fluctuations.
  • * The PSO-BPNN model provides a more accurate RSSI distance fit compared to traditional logarithmic models.
  • * Experimental results demonstrate superior positioning accuracy compared to traditional Bluetooth positioning methods.

Conclusions:

  • * The developed RSSI real-time correction method effectively mitigates errors in Bluetooth-based indoor positioning.
  • * The PSO-BPNN model enhances the accuracy of distance estimation from RSSI in complex indoor environments.
  • * This approach offers a promising solution for achieving high-accuracy indoor positioning.