A Dual-Adaptive Sage-Husa Kalman Filter with Model Matching Check for GNSS/INS-Integrated Navigation

Area of Science:

• Navigation Systems Engineering
• Signal Processing
• Control Theory

Background:

• Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integration is crucial for vehicle navigation.
• Urban environments present challenges like time-varying GNSS noise and dynamic motion mismatches.
• Conventional Sage-Husa filters struggle with performance degradation in these complex scenarios.

Purpose of the Study:

• To develop an advanced filtering algorithm for GNSS/INS integration in complex environments.
• To address the limitations of conventional filters in handling time-varying noise and model mismatch.
• To improve the stability and accuracy of navigation systems under adverse conditions.

Main Methods:

• Proposed a dual-adaptive Sage-Husa filtering algorithm incorporating model matching.
• Integrated a forgetting-factor-based global recursive estimation with an adaptive sliding window.
• Introduced a model matching test and measurement noise covariance upper-bound constraint.

Main Results:

• Reduced up-channel RMS error by 8.5% under high-dynamic motions and GNSS interference compared to cSage-Husa.
• Achieved significant RMS error reductions in north and up-channel directions under sustained GNSS degradation.
• Demonstrated mitigation of over-adaptation and convergence degradation, maintaining filter stability.

Conclusions:

• The proposed dual-adaptive filter enhances GNSS/INS navigation performance in urban and high-dynamic conditions.
• The algorithm effectively balances time-varying noise tracking with filtering stability.
• This approach offers improved reliability for intelligent transportation and unmanned systems.