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GROF: Indoor Localization Using a Multiple-Bandwidth General Regression Neural Network and Outlier Filter.

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  • 1College of Communications Engineering, PLA Army Engineering University, Nanjing 210007, China. dcarp@126.com.

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Summary

A new method called GROF improves indoor positioning accuracy by using a generalized regression neural network with multiple bandwidths and an outlier filter. This approach enhances robustness against environmental changes without needing extra training data.

Keywords:
K-nearest-neighbor (KNN)fingerprintinggeneral regression neural network (GRNN)indoor localizationreceived signal strength (RSS)

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Area of Science:

  • Engineering
  • Computer Science
  • Signal Processing

Background:

  • Indoor localization methods often rely on received signal strength (RSS) fingerprints.
  • RSS is sensitive to environmental variations, degrading localization accuracy.
  • Current fingerprinting methods are time-consuming and labor-intensive.

Purpose of the Study:

  • To propose a novel, lightweight, and robust indoor positioning approach.
  • To enhance the accuracy and stability of fingerprint-based localization.
  • To address the limitations of traditional methods in dynamic environments.

Main Methods:

  • Developed a multiple-bandwidth generalized regression neural network (GRNN).
  • Integrated a k-nearest neighbor (KNN) based outlier filtering scheme.
  • Utilized a Universal Software Radio Peripheral (USRP) platform for experiments.

Main Results:

  • The proposed GROF method demonstrated superior localization accuracy compared to standard GRNN, KNN, and BPNN.
  • GROF showed improved robustness against environmental fluctuations.
  • The method achieved better performance without requiring additional training samples.

Conclusions:

  • The GROF approach offers a more flexible and robust regression performance for indoor positioning.
  • It effectively mitigates accuracy degradation caused by environmental changes.
  • GROF presents a practical and efficient solution for lightweight indoor positioning applications.