An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems

Area of Science:

• Navigation Systems Engineering
• Control Theory
• Signal Processing

Background:

• Cubature Kalman Filter (CKF) is crucial for GPS/INS integrated navigation.
• CKF performance degrades with process uncertainties, leading to inaccuracy or divergence.

Purpose of the Study:

• Propose the improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF).
• Enhance robustness and accuracy of GPS/INS navigation systems under dynamic model errors.

Main Methods:

• Integrate improved strong tracking Kalman filter (ISTKF) with seventh-degree spherical simplex-radial cubature Kalman filter (7thSSRCKF).
• Employ hypothesis testing to identify process uncertainty.
• Modify prior state estimate covariance online based on vehicle dynamics.
• Utilize a novel seventh-degree spherical simplex-radial rule for improved estimation.

Main Results:

• The IST-7thSSRCKF algorithm effectively mitigates process uncertainty effects.
• The proposed filter demonstrates superior accuracy compared to standard CKF and ST-CKF.
• The IST-7thSSRCKF shows enhanced robustness in GPS/INS navigation with dynamic model errors.

Conclusions:

• The IST-7thSSRCKF successfully combines high accuracy and robustness.
• This algorithm offers a significant improvement for GPS/INS integrated navigation systems facing uncertainties.