A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation

Area of Science:

• Navigation Systems
• Signal Processing
• Robust Statistics

Background:

• Residual-based (RB) receiver autonomous integrity monitoring (RAIM) is crucial for enhancing navigation system integrity.
• Traditional RB RAIM methods struggle with sensitivity and vulnerability to multiple outliers.
• Least squares (LS) estimation in RB RAIM can be compromised by outliers, necessitating robust alternatives.

Purpose of the Study:

• To develop a modified RB RAIM detector with improved performance in multi-outlier environments.
• To introduce a fast subset selection method to optimize computational efficiency.
• To enhance the reliability and accuracy of integrity monitoring in GNSS receivers.

Main Methods:

• Implemented a robust MM estimation technique within the RB RAIM framework.
• Developed a novel fast subset selection algorithm utilizing characteristic slopes.
• Compared the proposed method against LS and M-estimation (IGG III function) based RB RAIM detectors.

Main Results:

• The proposed MM estimation-based RB RAIM detector demonstrated superior performance in multi-outlier scenarios compared to LS and M-estimators.
• The fast subset selection method reduced calculation time by over 80%.
• The algorithm maintained robust performance even with an increasing number of outliers.

Conclusions:

• The modified RB RAIM detector using robust MM estimation offers enhanced integrity monitoring capabilities, particularly under challenging multi-outlier conditions.
• The fast subset selection method significantly improves computational efficiency, making the algorithm practical for real-world applications.
• This robust approach provides a more reliable solution for GNSS integrity monitoring.