GNSS Landing Anomaly Detection via Inertial Deviation Comparison
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Solution Overview
Problem
Current Global Navigation Satellite System (GNSS) landing systems face challenges in detecting and addressing anomalies, particularly ramp errors and steps caused by satellite configuration changes, which can lead to inaccurate navigation and safety issues during category II and III operations.
Innovation Solution
A method and apparatus for detecting data anomalies in GNSS landing systems by comparing the magnitude and rate differences between blended inertial deviations and raw deviations, using coast-skip reset trigger and slow ramp detectors to identify and mitigate anomalies, thereby enabling extended inertial coasting and ensuring accurate navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If the coasting duration is extended to 60 seconds or more to allow approach completion during signal loss, then the ability to complete approach during GLS signal outage is improved, but the risk of incorporating corrupted inertial references increases
Solution Approach 1:
The patent implements a feedback mechanism where the anomaly detector continuously monitors GLS deviation data and compares it against expected parameters. When anomalies are detected, the system provides feedback to switch from GLS guidance to inertial coasting mode, and later feedback to reset the coasting filter when anomalies cease, ensuring the inertial reference is not corrupted during extended coasting periods.
Solution Approach 2:
The anomaly detector acts as an intermediary between the GLS guidance system and the inertial coasting filter. It monitors GLS data quality and mediates the transition between GLS-based guidance and inertial coasting, preventing corrupted GLS data from corrupting the inertial reference while enabling extended coasting duration when needed.
2Measurement precision
If anomaly detection sensitivity is increased to detect all detrimental error rates, then the detection accuracy is improved, but the false alarm rate increases due to normal satellite configuration changes
Solution Approach 1:
The patent applies local quality by implementing different detection thresholds and criteria for different types of anomalies. The anomaly detector analyzes specific characteristics of GLS deviation data (such as rate of change, magnitude, and pattern) to distinguish between harmful anomalies requiring coasting and normal satellite configuration changes, allowing sensitive detection without excessive false alarms.
Solution Approach 2:
The system dynamically adjusts detection parameters based on the operational context. The anomaly detector monitors multiple parameters of GLS data (deviation magnitude, rate of change, consistency with inertial predictions) and changes its detection sensitivity based on the observed data characteristics, enabling accurate detection of detrimental errors while tolerating normal satellite configuration variations.
3Measurement precision
If the coast-skip reset filter is continuously applied to maintain accurate inertial reference, then the inertial coasting precision is improved, but the system becomes vulnerable to corruption from undetected low-frequency errors
Solution Approach 1:
The patent implements preliminary action by having the anomaly detector continuously analyze GLS data before it can corrupt the inertial reference. The system proactively detects potential errors in GLS deviation data and triggers coasting mode in advance, preventing the corruption of inertial references before it occurs, rather than reacting after corruption has happened.
Solution Approach 2:
The coast-skip reset filter provides continuous useful action by maintaining the inertial coasting capability throughout the approach. The filter continuously integrates inertial measurements and periodically resets using GLS data when verified clean, ensuring continuous accurate inertial reference availability while protecting against corruption through the anomaly detection and reset mechanism.
Data Source
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AI summary
A method, apparatus, and computer program product for detecting anomalies in a landing system. In one embodiment, a magnitude difference between a blended inertial deviation magnitude and a raw deviation magnitude is identified to form a magnitude difference. The magnitude difference is compared to a magnitude threshold. If the magnitude difference exceeds the magnitude threshold, an anomaly in the data is detected.