Radar tracking with an interacting multiple model and probabilistic data association filter for civil aviation...

Area of Science:

• Aviation Technology
• Navigation Systems
• Signal Processing

Background:

• Modern air traffic management (ATM) relies on global positioning system (GPS) technology, posing risks due to signal vulnerability to radio-frequency interference.
• GPS signal limitations necessitate backup systems for uninterrupted air traffic control (ATC) and navigation services.

Purpose of the Study:

• To address GPS signal interruption concerns in ATM systems.
• To enhance the tracking performance of existing radar systems for ATM applications.
• To propose a robust alternative navigation and surveillance solution during GPS outages.

Main Methods:

• Development of an enhanced tracking algorithm: Interacting Multiple Model and Probabilistic Data Association Filter (IMMPDAF).
• Utilizing the Nearest Neighbor Kalman Filter (NNKF) as a baseline for performance comparison.
• Validation of the IMMPDAF algorithm using real flight data.

Main Results:

• The IMMPDAF algorithm significantly improves the tracking performance of current aviation radar systems.
• The enhanced algorithm meets the stringent performance requirements of the new ATM system.
• Demonstrated superior tracking accuracy compared to the conventional NNKF algorithm.

Conclusions:

• The IMMPDAF algorithm provides a reliable enhancement for aviation radar tracking.
• The radar system integrated with IMMPDAF serves as a critical alternative for ATM navigation and surveillance during GPS outages.
• Ensures continued aviation safety and operational integrity despite GPS vulnerabilities.