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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Ranging accuracy is crucial for time-based indoor positioning systems.
  • Complex indoor environments present challenges for achieving centimeter-level ranging accuracy.
  • Multipath effects significantly degrade the performance of traditional Received Signal Strength Indicator (RSSI)-based ranging methods.

Purpose of the Study:

  • To propose a novel distance measurement technique to mitigate ranging errors in multipath environments.
  • To enhance the accuracy of indoor positioning systems by addressing signal propagation challenges.
  • To develop a method that combines RSSI and Angle of Arrival (AoA) for improved ranging.

Main Methods:

  • Development of an artificial neural network (ANN) model.
  • Utilizing both Received Signal Strength Indicator (RSSI) and Angle of Arrival (AoA) as inputs to the ANN.
  • Calculating the line-of-sight (LOS) distance based on the ANN's output.
  • Comparing the proposed method against traditional ranging techniques.

Main Results:

  • Significant reduction in ranging error caused by multipath effects.
  • The proposed ANN-based method demonstrates superior performance compared to conventional ranging approaches.
  • The method achieves high accuracy while maintaining low implementation complexity.

Conclusions:

  • The novel ANN approach effectively reduces ranging errors in challenging indoor environments.
  • Combining RSSI and AoA provides a robust solution for accurate distance measurement in the presence of multipath interference.
  • This method offers a practical and efficient way to improve indoor positioning accuracy.