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A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System
Yushi Hao1,2, Aigong Xu3, Xin Sui4
1School of Geomatics, Liaoning Technical University, Fuxin 123000, China. kinghaoys@163.com.
Sensors (Basel, Switzerland)
|November 9, 2018
Summary
This study introduces an adaptive extended Kalman filter (EKF) to enhance inertial navigation system (INS) and Global Positioning System (GPS) integration. The method improves accuracy and stability in challenging environments by adapting noise statistics.
Area of Science:
- Navigation systems engineering
- Signal processing and estimation theory
- Robust control systems
Background:
- Inertial Navigation System (INS) and Global Positioning System (GPS) integration is crucial for accurate navigation, especially in challenging environments.
- Accurate estimation relies heavily on precise knowledge of state process and measurement noise statistics.
- Existing methods can suffer from numerical issues due to measurement outliers and dynamic model errors.
Purpose of the Study:
- To propose a modified extended Kalman filter (EKF) that adaptively estimates noise statistics for improved INS/GPS integration.
- To address numerical problems and enhance estimation stability and accuracy in harsh navigation conditions.
- To demonstrate the robustness of the proposed method against measurement outliers and dynamic model uncertainties.
Main Methods:
- Implementation of an innovation-based adaptive estimation (IAE) method to adjust state process and observation noise covariances.
- Development of a remodification algorithm for updating measurement noise covariance based on reliability.
- Integration of a two-antenna GPS receiver with the INS for enhanced data.
- Experimental field testing to validate the proposed adaptive EKF.
Main Results:
- The proposed adaptive EKF effectively mitigates numerical issues caused by measurement outliers.
- Adaptation of noise statistics using IAE leads to more stable and accurate state estimates.
- The remodification algorithm enhances measurement reliability, further improving estimation performance.
- Field tests confirmed the method's robustness against dynamic model errors.
Conclusions:
- The adaptive EKF with IAE and a remodification algorithm offers a robust solution for INS/GPS integration in adverse conditions.
- Proper adaptation of noise statistics is critical for overcoming challenges posed by measurement outliers and model uncertainties.
- The proposed method significantly enhances the reliability and accuracy of navigation systems.
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