Kalman Filter-Based RAIM for Reliable Aircraft Positioning with GPS and NavIC Constellations

Area of Science:

• Aerospace Engineering
• Navigation Systems
• Signal Processing

Background:

• Reliable aircraft positioning is critical for aviation safety.
• Global Navigation Satellite Systems (GNSS) are susceptible to signal loss and integrity issues.
• Existing Receiver Autonomous Integrity Monitoring (RAIM) algorithms have limitations in computational load and error bound management.

Purpose of the Study:

• To develop and validate a novel Kalman filter (KF)-based RAIM algorithm for enhanced GNSS integrity monitoring in aircraft.
• To address limitations of previous KF RAIM approaches, particularly concerning position error bounds and computational efficiency.
• To evaluate the performance of the new algorithm using dual-constellation GNSS (NavIC and GPS) over the Indian subcontinent.

Main Methods:

• Development of a KF-based RAIM algorithm in the range domain.
• Comparison with existing weighted least squares navigation filter and KF (position domain) RAIM methods.
• Extensive simulations using an unmanned aircraft flight path over the Indian subcontinent.

Main Results:

• The proposed range-domain KF RAIM algorithm demonstrates satisfactory performance with moderate computational requirements, mitigating the issue of growing position error bounds.
• While existing methods showed superior performance in simulations, the new KF RAIM offers a practical alternative for real-time avionics implementation.
• Integrating NavIC with GPS significantly enhances RAIM performance, particularly in scenarios with poor satellite geometry.

Conclusions:

• The developed range-domain KF RAIM algorithm presents a promising solution for reliable and computationally efficient aircraft positioning.
• The study highlights the importance of KF-based RAIM for advanced navigation systems facing GNSS signal interruptions.
• Combining multiple GNSS constellations, such as NavIC and GPS, is crucial for improving integrity monitoring robustness and performance.