Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations

Area of Science:

• Navigation Systems
• Aerospace Engineering
• Signal Processing

Background:

• Global Navigation Satellite Systems (GNSS) are crucial for modern navigation.
• Receiver Autonomous Integrity Monitoring (RAIM) ensures the reliability of GNSS.
• Kalman filters (KF) are essential for advanced navigation methods.

Purpose of the Study:

• Evaluate a KF-based RAIM algorithm for GNSS integrity monitoring.
• Improve fault detection capabilities, especially for slow ramp faults.
• Enhance the reliability of GNSS for advanced applications like multi-sensor integration.

Main Methods:

• Developed and integrated realistic carrier-smoothed pseudorange error models into KF RAIM.
• Modified the error covariance matrix to capture temporal error variations.
• Analyzed performance using simulated GPS and Indian constellation signals during aircraft flight phases.

Main Results:

• The modified KF RAIM, utilizing Schmidt KF (SKF), successfully detects slow ramp faults missed by innovation-based tests with extended KF.
• The proposed algorithm demonstrates lower protection levels compared to Weighted Least Squares (WLS) RAIM.
• KF RAIM processing completes in real-time on low-end computers.

Conclusions:

• KF RAIM with SKF is a promising solution for range-domain integrity monitoring in GNSS.
• The modified algorithm enhances the reliability of advanced navigation systems.
• This work addresses limitations in prior KF RAIM approaches by incorporating realistic error models.