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An Improved Adaptive Kalman Filter Positioning Method Based on OTFS.

Siqi Xia1, Aijun Liu1, Xiaohu Liang1

  • 1School of Communication Engineering, Army Engineering University, Nanjing 210007, China.

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|October 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved adaptive Kalman filter for 6G mobile systems, enhancing positioning accuracy in dynamic line-of-sight (LOS) and non-line-of-sight (NLOS) environments using Orthogonal Time Frequency Space (OTFS) signals.

Keywords:
adaptive Kalman filtercomplex environmentspositioningsixth-generation mobile communications systems (6G)

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Area of Science:

  • Mobile Communications
  • Signal Processing
  • Navigation Systems

Background:

  • Sixth-generation (6G) mobile systems face positioning accuracy challenges due to dynamic line-of-sight (LOS) and non-line-of-sight (NLOS) conditions.
  • Existing positioning methods struggle to maintain accuracy in complex, mixed LOS/NLOS environments.

Purpose of the Study:

  • To develop an improved adaptive Kalman filter positioning method for 6G systems.
  • To enhance positioning accuracy and stability in dynamic LOS and NLOS conditions.
  • To leverage Orthogonal Time Frequency Space (OTFS)-modulated signals for precise distance measurements.

Main Methods:

  • Utilized OTFS-modulated signals for distance measurement between base stations and nodes.
  • Converted distance information to distance difference for Time Difference of Arrival (TDOA) positioning equation establishment.
  • Employed the Chan algorithm for preliminary positioning and calculated residuals.
  • Adaptively determined genetic factors for different environmental regions based on residuals.
  • Applied Sage-Husa adaptive Kalman filter with selected genetic parameters for final positioning estimation.

Main Results:

  • The proposed method demonstrated improved accuracy in estimating vehicle motion trajectories compared to actual trajectories in complex LOS/NLOS environments.
  • Significant enhancements in both the accuracy and stability of positioning results were observed.
  • The adaptive approach effectively handled environmental variations by adjusting genetic factors.

Conclusions:

  • The improved adaptive Kalman filter based on OTFS signals effectively mitigates positioning degradation in dynamic 6G environments.
  • The method offers superior accuracy and stability over traditional approaches in mixed LOS/NLOS conditions.
  • This work contributes a robust positioning solution for future mobile communication systems.