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FFK: Fourier-Transform Fuzzy-c-means Kalman-Filter Based RSSI Filtering Mechanism for Indoor Positioning.

Chinyang Henry Tseng1, Woei-Jiunn Tsaur2

  • 1Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 23741, Taiwan.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary

This study introduces a novel Fouriertransform Fuzzyc-means Kalmanfilter (FFK) for Received Signal Strength Indication (RSSI) fingerprinting in indoor positioning. FFK enhances distance estimation accuracy by stabilizing RSSI values, outperforming existing methods.

Keywords:
Fourier transformKalman filterfuzzy c-meansindoor positioningreceived signal strength indication

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Area of Science:

  • Signal Processing
  • Indoor Positioning Systems
  • Internet of Things

Background:

  • Received Signal Strength Indication (RSSI) fingerprinting is common for indoor positioning.
  • Existing methods often use Gaussian and Kalman filters for RSSI fingerprinting.
  • RSSI value distributions can be non-Gaussian, limiting current filter performance.

Purpose of the Study:

  • To propose a novel Fouriertransform Fuzzyc-means Kalmanfilter (FFK) for stable RSSI fingerprinting.
  • To improve distance estimation accuracy in indoor positioning systems.
  • To address the limitations of arbitrary RSSI distributions in traditional filtering methods.

Main Methods:

  • Developed the FFK mechanism, integrating Fourier transform, Fuzzy C-Means (FCM), and Kalman filter.
  • Utilized Fourier transform to extract stable RSSI values from the low-frequency domain.
  • Employed FCM to identify Line of Sight (LOS) clusters in arbitrary RSSI distributions, followed by Kalman filtering for stable value estimation.

Main Results:

  • FFK demonstrated superior distance estimation accuracy compared to Gaussian filters, Kalman filters, and their combinations.
  • The proposed FFK method effectively handles arbitrary RSSI distributions.
  • Experimental results in a realistic environment validated the enhanced accuracy of FFK.

Conclusions:

  • The FFK filtering mechanism provides a more stable RSSI fingerprint for accurate indoor positioning.
  • FFK offers a significant improvement over conventional methods for RSSI-based distance estimation.
  • This approach enhances the reliability of indoor positioning applications within the Internet of Things.