Adaptive filter parameter reconstruction technology for rocket inertial navigation/satellite integrated navigation...

Area of Science:

• Navigation Systems Engineering
• Aerospace Engineering
• Signal Processing

Background:

• Micro-electro-mechanical systems (MEMS) strapdown inertial navigation systems (SINS) integrated with global navigation satellite systems (GNSS) offer compact, affordable, high-precision navigation.
• Rocket-borne MEMS-SINS/GNSS systems require high overload, accuracy, and real-time performance, posing challenges due to changing MEMS noise and dynamic flight environments.
• Traditional Kalman filtering methods struggle with adaptive parameter modeling for real-time navigation under high overload.

Purpose of the Study:

• To develop an advanced filtering method for MEMS-SINS/GNSS integrated navigation systems on rockets.
• To address the challenges of noise variations and real-time demands in rocket flight navigation.
• To improve the accuracy and real-time capability of navigation solutions under high overload conditions.

Main Methods:

• Development of a precise system state model tailored to rocket flight dynamics.
• Implementation of real-time filter parameter reconstruction during the rocket alignment phase.
• Introduction of the Adaptive Reconfigurable Extended Kalman Filter (AREKF) algorithm.

Main Results:

• The AREKF method demonstrates rapid convergence of the filtering process.
• Adaptive modeling of navigation parameters ensures lower computational costs and enhanced accuracy.
• AREKF significantly improves real-time navigation accuracy compared to traditional EKF and other improved algorithms in high overload scenarios.

Conclusions:

• The AREKF method provides a robust solution for high-overload, real-time navigation in rocket-borne MEMS-SINS/GNSS systems.
• This approach effectively models changing noise characteristics and dynamic flight environments.
• AREKF enhances navigation precision and real-time output, validated through simulations and experiments.