Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter.

Area of Science:

• Navigation Systems Engineering
• Signal Processing
• Control Theory

Background:

• Kalman filtering (KF) suffers from error diffusion with model mismatch or noise.
• Multiple Fading Factors Kalman Filter (MFKF) accuracy can decrease due to overuse of fading factors.
• Existing methods for judging filtering anomalies lack accuracy and adaptability.

Purpose of the Study:

• To propose an improved integrated navigation algorithm addressing KF and MFKF limitations.
• To enhance the robustness and self-adaptability of navigation systems.
• To improve positioning accuracy in integrated inertial navigation system (INS)/global navigation satellite system (GNSS) applications.

Main Methods:

• Developed an integrated navigation algorithm based on a novel multiple fading factors Kalman filter (MFKF).
• Implemented an improved basis for judging filtering anomalies using innovation covariance theory.
• Introduced a recursive update for innovation covariance estimation and an empirical reserve factor.
• Utilized an INS/GNSS integrated navigation model for performance evaluation.

Main Results:

• The proposed algorithm demonstrated improved average positioning accuracy by 26.52% over KF.
• The algorithm showed a 7.48% improvement in average positioning accuracy compared to traditional MFKF.
• The enhanced filter exhibited superior robustness and self-adaptability in navigation tasks.

Conclusions:

• The novel MFKF algorithm effectively mitigates error diffusion and improves accuracy in INS/GNSS integration.
• The improved anomaly detection and adaptive fading factor introduction enhance filter reliability.
• The proposed method offers a more robust and accurate solution for integrated navigation systems.