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Research on GNSS/MEMS IMU Array Fusion Localization Method Based on Improved Grey Prediction Model.

Yihao Chen¹, Jieyu Liu¹, Weiwei Qin²

¹College of Missile Engineering, Rocket Force University of Engineering, Xi'an 710025, China.

|September 27, 2025

Summary

This summary is machine-generated.

This study introduces an improved grey prediction model for vehicle navigation, enhancing Global Navigation Satellite System (GNSS)/MEMS IMU fusion. The method boosts positioning accuracy, especially during GNSS signal denial.

Keywords:

GNSS denial MEMS IMU arrays adaptive fusion grey prediction model vehicle navigation

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Area of Science:

Robotics
Navigation Systems
Signal Processing

Background:

Global Navigation Satellite System (GNSS) signals are prone to interference and blockage in vehicle navigation, degrading positioning accuracy.
Traditional grey prediction models struggle with the complex motion data characteristic of vehicles.
Accurate real-time positioning is critical for autonomous and assisted driving systems.

Purpose of the Study:

To develop an enhanced GNSS/MEMS IMU fusion localization method for vehicles.
To improve positioning accuracy and reliability, particularly under GNSS signal denial conditions.
To overcome the limitations of existing grey prediction models in dynamic environments.

Main Methods:

A multi-feature fusion GNSS confidence evaluation algorithm assesses GNSS data reliability in real-time.
An improved grey prediction model incorporates dynamic background value optimization and residual sequence compensation for complex motion data.
An adaptive fusion framework integrates GNSS and MEMS IMU data, using grey model predictions as virtual measurements during GNSS outages.

Main Results:

The improved grey prediction model achieved 31%, 52%, and 45% higher accuracy than the traditional GM(1,1) model in straight, turning, and acceleration scenarios, respectively.
Positioning accuracy improved by over 79% compared to pure Inertial Navigation System (INS) methods during a 30-second GNSS denial period.
The proposed method demonstrated enhanced sensitivity to vehicle motion state changes and improved nonlinear motion prediction.

Conclusions:

The proposed GNSS/MEMS IMU fusion method significantly enhances vehicle positioning accuracy and robustness.
The improved grey prediction model effectively handles complex vehicle dynamics and GNSS signal disruptions.
This approach offers a reliable solution for navigation systems facing intermittent or denied GNSS availability.