An Improved Adaptive Kalman Filter Positioning Method Based on OTFS

TL;DR

This paper introduces a new positioning method using OTFS signals and adaptive filters to improve accuracy in complex communication environments.

Contribution

A novel adaptive Kalman filter method is proposed using OTFS signals for improved positioning in mixed LOS/NLOS conditions.

Findings

The proposed method improves positioning accuracy in complex environments with LOS and NLOS conditions.

Vehicle motion trajectories estimated using the method closely match actual trajectories.

Positioning results show higher accuracy and stability compared to traditional methods.

Abstract

To mitigate the degradation of positioning accuracy in sixth-generation mobile communication systems under dynamic line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, this paper proposes an improved adaptive Kalman filter positioning method based on Orthogonal Time Frequency Space (OTFS)-modulated signals. Firstly, the distance can be measured by using the OTFS-modulated signals transmitted between base stations and nodes. Secondly, the distance information is converted into the distance difference information to establish the time difference of arrival (TDOA) positioning equation, which is preliminarily solved using the Chan algorithm. Thirdly, residuals are calculated based on the preliminary positioning results, dividing the complex environment into distinct regions and adaptively determining corresponding genetic factors for each region. Finally, the selected genetic parameters are substituted into the Sage–Husa adaptive Kalman filter equations to estimate positioning results. The simulation analysis demonstrates that in complex environments featuring both line-of-sight and non-line-of-sight conditions, the vehicle motion trajectories estimated using this method more closely approximate actual trajectories. Additionally, both the accuracy and stability of positioning results show significant improvement compared to traditional methods.

Figures