Simulation Experiment and Analysis of GNSS/INS/LEO/5G Integrated Navigation Based on Federated Filtering Algorithm

Area of Science:

• Navigation Systems Engineering
• Satellite Communications
• Geospatial Positioning

Background:

• China's new infrastructure initiatives drive the need for advanced navigation solutions.
• Traditional satellite navigation systems face limitations in certain environments.
• Integration of diverse signal sources is crucial for robust positioning.

Purpose of the Study:

• To evaluate the positioning enhancement provided by integrating Low Earth Orbit (LEO) constellation and 5G ranging signals.
• To analyze the performance of tightly coupled Kalman federated filters in multi-signal navigation.
• To investigate methods for assigning information factors in federated filtering for sensors with varying precision.

Main Methods:

• Simulation of LEO constellation and 5G ranging signal sources for integrated navigation.
• Implementation of a tightly coupled Kalman federated filter algorithm.
• Simulation of diverse experimental scenarios by varying azimuth and elevation angles to test positioning accuracy and continuity.

Main Results:

• Integration of LEO and 5G signals improved positioning accuracy of original INS/GNSS by an order of magnitude.
• High positioning continuity was maintained with the addition of new signal sources.
• Federated filtering demonstrated adaptability to combined navigation modes across different scenarios and sensor precisions.

Conclusions:

• LEO constellation and 5G ranging signals offer substantial improvements for integrated navigation systems.
• The federated filtering approach effectively manages and fuses data from heterogeneous sensors.
• This research provides a method for optimizing information factor allocation in complex navigation scenarios.