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An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning.

Jingmin Yang1,2,3, Shanghui Deng1,2, Minmin Lin1,2

  • 1School of Computer Science, Minnan Normal University, Zhangzhou 363000, China.

Sensors (Basel, Switzerland)
|August 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive calibration ranging algorithm to improve indoor positioning accuracy. The new method enhances ranging accuracy by 58% and reduces positioning error by 69% compared to classic models.

Keywords:
adaptive calibrationerror correction linear regression equationindoor ranging and positioninglogarithmic distance path loss modelreceived signal strength indication

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Area of Science:

  • Engineering
  • Computer Science
  • Signal Processing

Background:

  • Received Signal Strength Indication (RSSI) and Logarithmic Distance Path Loss Model (LDPLM) are common for indoor positioning.
  • Classic LDPLM suffers from reduced ranging accuracy due to fixed coefficients and error estimation.
  • Limited research addresses LDPLM's limitations in accuracy for indoor positioning.

Purpose of the Study:

  • To propose an adaptive calibration ranging algorithm based on LDPLM to enhance indoor positioning accuracy.
  • To address the limitations of fixed coefficients and error estimation in the classic LDPLM.
  • To improve the reliability and precision of RSSI-based indoor positioning systems.

Main Methods:

  • Developed an adaptive calibration ranging algorithm comprising coefficient adaptation and error correction.
  • Utilized error theory and the least squares method for coefficient adaptation.
  • Employed linear regression with least squares for error correction and Gaussian filtering for RSSI fluctuation reduction.

Main Results:

  • The proposed adaptive algorithm significantly improved ranging accuracy.
  • Ranging accuracy was enhanced by 58% over classic LDPLM and 51% over polynomial fitting.
  • Positioning cumulative prediction error decreased by 69% (vs. classic LDPLM) and 80% (vs. polynomial fitting).

Conclusions:

  • The adaptive calibration ranging algorithm effectively overcomes the limitations of classic LDPLM.
  • The proposed method offers superior ranging accuracy and reduced positioning error for indoor environments.
  • This algorithm provides a more robust and precise solution for RSSI-based indoor positioning.