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Dynamic Modeling and Its Impact on Estimation Accuracy for GPS Navigation Filters.

Dah-Jing Jwo¹, Ta-Shun Cho², Birhanu Ayalew Demssie¹

¹Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, 2 Peining Road, Zhongzheng, Keelung 202301, Taiwan.

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Summary

This summary is machine-generated.

This study enhances GPS navigation by using fictitious process noise in the extended Kalman filter (EKF) to overcome dynamic modeling inaccuracies. This method improves trajectory tracking and navigation accuracy in various motion scenarios.

Keywords:

divergence extended Kalman filter global positioning system modeling

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Area of Science:

Navigation Systems
Estimation Theory
Signal Processing

Background:

Kalman filter (KF) and extended Kalman filter (EKF) algorithms rely on process models that often diverge from real-world conditions, degrading real-time navigation performance.
Inaccuracies in dynamic modeling are a primary cause of divergence issues in Global Positioning System (GPS) navigation.

Purpose of the Study:

To address divergence issues in GPS navigation systems caused by dynamic modeling inaccuracies.
To introduce and evaluate fictitious process noise as a solution to mitigate EKF divergence.
To enhance the observability and controllability of GPS navigation filters.

Main Methods:

The study employs the extended Kalman filter (EKF) with the introduction of fictitious process noise.
Covariance estimation and tuning factors are utilized to improve filter performance.
Simulations include various motion scenarios: stationary receivers, straight-line trajectories (constant/varying speeds), and turning trajectories.
Position (P), position-velocity (PV), and position-velocity-acceleration (PVA) models are evaluated using simulated receivers.

Main Results:

The inclusion of process noise enables the EKF to adapt to changes in speed and direction without explicit dynamic modeling.
Simulations demonstrate improved vehicle trajectory tracking compared to Ordinary Least-Squares (OLS) methods.
The EKF with fictitious process noise shows enhanced statistical reliability and real-world performance across diverse scenarios.

Conclusions:

Fictitious process noise is an effective remedy for mitigating divergence in EKF-based GPS navigation systems.
The proposed approach offers a systematic method to improve estimation accuracy and broaden the applicability of navigation systems.
This research provides valuable insights for enhancing the robustness and precision of GPS navigation filter designs.