INS/CNS Deeply Integrated Navigation Method of Near Space Vehicles

Area of Science:

• Aerospace Engineering
• Navigation Systems
• Control Theory

Background:

• Near space vehicles require accurate long-term navigation, but traditional celestial navigation is slow and susceptible to aero-optical effects.
• Aero-optical effects in the near space environment introduce noise, degrading integrated navigation filter accuracy.
• Limitations in star identification speed and measurement noise hinder the dynamic response and accuracy of current navigation systems.

Purpose of the Study:

• To propose a novel INS/CNS deeply integrated navigation method for near space vehicles.
• To address the challenges of slow star identification and aero-optical effects in near space navigation.
• To improve the dynamic response ability and filtering accuracy of navigation systems in the near space environment.

Main Methods:

• Developed a deeply integrated INS/CNS navigation model optimizing attitude using a gray image error function for star-independent estimation.
• Implemented a second-order state augmented H-infinity filter to whiten measurement noise caused by aero-optical effects.
• Utilized state augmentation algorithms to enhance filter performance under near space environmental conditions.

Main Results:

• The proposed method reduces computational cost by 50% while maintaining attitude accuracy within 10 arcseconds.
• The second-order state augmented H-infinity filter achieves an attitude root mean square error below 5 arcseconds, even with 50% parameter error.
• Demonstrated significant improvements in rapid response capability and filtering accuracy for near space navigation.

Conclusions:

• The INS/CNS deep integration method effectively enhances navigation system performance in near space.
• The approach provides a viable solution for improving dynamic response and accuracy under challenging environmental conditions.
• Offers a valuable reference for the design of future near space vehicle navigation systems.