Sensor Fusion of Gaussian Mixtures for Ballistic Target Tracking in the Re-Entry Phase

Area of Science:

• Aerospace Engineering
• Signal Processing
• Control Systems

Background:

• Ballistic target tracking is crucial for defense applications.
• Accurate tracking of re-entry vehicles is challenging due to unknown ballistic coefficients.
• Existing methods may suffer from estimation inaccuracies.

Purpose of the Study:

• To develop an improved sensor fusion methodology for ballistic target tracking.
• To enhance estimation accuracy using a track-to-track fusion architecture.
• To address challenges posed by unknown ballistic coefficients.

Main Methods:

• Proposed a sensor fusion methodology based on the Gaussian mixtures model.
• Implemented a track-to-track fusion architecture.
• Utilized interacting multiple model filters for local track generation.
• Incorporated a method to remove duplicate information by considering first-order redundant information.

Main Results:

• The proposed algorithm significantly improves tracking accuracy.
• Demonstrated enhanced performance in ballistic target tracking simulations.
• Successfully handled targets with unknown ballistic coefficients.

Conclusions:

• The developed sensor fusion methodology is effective for ballistic target tracking.
• The track-to-track fusion architecture enhances estimation accuracy.
• The approach is particularly beneficial for re-entry phase applications.