A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter.

Area of Science:

• Aerospace Engineering
• Navigation Systems
• Signal Processing

Background:

• Autonomous aerial vehicle navigation requires high reliability and accuracy.
• Integrating multiple navigation systems (INS, SRS, CNS) can improve performance.
• Robustness against measurement noise and outliers is crucial for real-world applications.

Purpose of the Study:

• To design an integrated navigation system for aerial vehicles using INS, SRS, and CNS.
• To develop a spectral-redshift-based velocity measurement equation for this integrated system.
• To propose a chi-square test-based robust Kalman filter (CSTRKF) for enhanced navigation system robustness.

Main Methods:

• Design of an INS/SRS/CNS integrated navigation system.
• Derivation of a spectral-redshift-based velocity measurement equation.
• Development and application of a chi-square test-based robust Kalman filter (CSTRKF).

Main Results:

• The proposed INS/SRS/CNS integrated system demonstrates effective navigation capabilities.
• The CSTRKF successfully identifies and mitigates measurement outliers and non-Gaussian noise.
• Simulations confirm the enhanced robustness and high reliability of the integrated system with CSTRKF.

Conclusions:

• The integrated INS/SRS/CNS navigation system offers a reliable solution for autonomous aerial vehicles.
• The CSTRKF significantly improves the robustness of the navigation system.
• This approach provides a foundation for highly autonomous and dependable aerial navigation.