MATLAB Implementation of Satellite Integrity Monitoring

Satellite integrity monitoring is a critical technology for ensuring the reliability of Global Navigation Satellite Systems (GNSS), primarily used to detect anomalies in satellite signals. Implementing this functionality in MATLAB typically involves the following core components:

Data Preprocessing The first step involves receiving satellite observation data (such as pseudorange and carrier phase) and navigation messages. Through time alignment and data validity checks, obvious outliers are removed to provide clean data sources for subsequent analysis. In MATLAB, this can be implemented using functions like readtable() for data ingestion and isoutlier() for statistical outlier detection.

Error Modeling and Threshold Calculation Error models are established based on satellite constellation types (GPS/BeiDou, etc.), considering factors like clock errors, orbital errors, and ionospheric delays. Error thresholds for each satellite signal are dynamically calculated using historical data or real-time statistical methods (such as sliding windows). MATLAB's fitlm() or mvregress() functions can be used for regression-based error modeling, while movmean() facilitates sliding window calculations.

Integrity Determination Logic The core logic compares whether the deviation between observed values and theoretical values exceeds thresholds. Common monitoring methods include: Receiver Autonomous Integrity Monitoring (RAIM): Uses redundant satellites for consistency checks to detect faulty satellites, often implemented through parity space algorithms or least-squares residual methods. Signal Quality Monitoring (SQM): Analyzes physical layer indicators like signal power and multipath effects, which can be programmed using signal processing toolbox functions for spectral analysis.

Result Visualization and Alerts Satellite status (normal/warning/fault) is displayed in real-time through MATLAB graphical interfaces, such as marking abnormal satellites on constellation diagrams or outputting log files to record fault time points. MATLAB's geoscatter() function can visualize satellite positions, while uialert() creates user notification dialogs.

Engineering considerations include: Real-time Optimization: Using matrix operations instead of loops to improve processing efficiency for large satellite constellations, leveraging MATLAB's vectorization capabilities. Multi-system Compatibility: Adapting to characteristic parameters of different satellite systems (e.g., special handling for BeiDou's GEO satellites) through configuration files or conditional programming. Test Validation: Using simulation data to inject typical faults (clock jumps, signal distortion) and verify algorithm sensitivity, which can be implemented with Aerospace Toolbox's satellite scenario simulations.

This implementation can be extended to Satellite-Based Augmentation Systems (SBAS) or integrated with Inertial Navigation Systems (INS) for fused integrity monitoring.