Information Fusion Based on Complementary Filter for SINS/CNS/GPS Integrated Navigation System of Aerospace Plane.

Area of Science:

• Aerospace Engineering
• Navigation Systems
• Signal Processing

Background:

• Federated Kalman Filter (FKF) methods for integrated navigation systems face challenges with heavy computational load and poor real-time performance.
• Strapdown Inertial Navigation Systems (SINS), Celestial Navigation Systems (CNS), and Global Positioning Systems (GPS) are critical for aerospace vehicle navigation.

Purpose of the Study:

• To propose a novel information fusion method using complementary filters (CF) for SINS/CNS/GPS integrated navigation systems on aerospace planes.
• To address the computational and real-time limitations of existing FKF-based fusion methods.

Main Methods:

• Designed complementary filters for attitude, velocity, and position estimation within the SINS/CNS/GPS integrated navigation system.
• Compared the performance of the CF-based method against the FKF method through simulations and experiments.

Main Results:

• The CF-based information fusion method effectively integrates SINS, CNS, and GPS data.
• CF demonstrated superior simplicity, reduced computation, enhanced real-time performance, and improved stability compared to FKF.
• Computational efficiency of the CF method was increased by 94.81%.

Conclusions:

• The proposed CF-based information fusion method is a viable and superior alternative to FKF for aerospace navigation.
• CF offers advantages such as no need for initial alignment and fast convergence, verified by semi-physical simulations and real-time experiments.