Dual Kalman Filter Navigation Integrity System
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Solution Overview
Problem
Aircraft navigation systems face inaccuracies and unavailability of location information due to faults in GNSS receivers, such as clock drift, satellite mispositioning, and signal blockages, which can lead to unreliable navigation during critical maneuvers like landing.
Innovation Solution
The navigation integrity system integrates inertial navigation with GNSS data using real-time and delay Kalman filters, leveraging satellite-based and ground-based augmentation systems to detect faults and switch between real-time and predictive location information, ensuring continuous accurate navigation by introducing a time delay to verify integrity and provide predictive location when real-time data becomes corrupted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS receiver is used to provide location information, then location accuracy is improved, but system reliability deteriorates due to potential faults in GNSS components
Solution Approach 1:
The system segments the navigation solution into multiple independent components: real-time Kalman filter processing, delayed Kalman filter processing, and fault detection modules. By dividing the navigation system into separate functional segments that can operate independently, the system maintains location accuracy through real-time processing while isolating potential faults to prevent complete system failure.
Solution Approach 2:
The system performs preliminary fault detection by comparing real-time location information with delayed location information before the fault can compromise navigation safety. The delayed Kalman filter processes historical data in advance to establish a baseline, enabling the system to detect anomalies and switch to safe mode proactively rather than reactively.
2Speed
If real-time location information is used for navigation, then navigation responsiveness is improved, but measurement reliability deteriorates when GNSS faults occur
Solution Approach 1:
The system implements feedback by continuously comparing real-time location information with delayed location information. The delayed Kalman filter provides feedback on what the location should be based on historical data, allowing the system to detect when real-time measurements deviate due to faults. This feedback mechanism maintains responsiveness while ensuring accuracy through cross-validation.
Solution Approach 2:
The delayed Kalman filter acts as an intermediary between the real-time measurements and the navigation system. It processes historical data to provide a reference solution that mediates the comparison with real-time data, enabling fault detection without compromising the responsiveness provided by real-time processing.
3Reliability
If dual Kalman filters with time delay are implemented, then fault detection capability is improved, but device complexity increases
Solution Approach 1:
The delayed Kalman filter serves multiple functions: it provides a reference solution for fault detection, maintains a historical baseline for comparison, and can serve as a backup navigation solution. By making this single component multi-functional, the system achieves improved fault detection capability without proportionally increasing complexity.
Solution Approach 2:
The system discards potentially faulty real-time measurements when faults are detected and recovers by switching to the delayed Kalman filter solution. This approach allows the system to maintain reliability by discarding bad data and recovering with a known good solution, achieving fault detection capability through a relatively simple switching mechanism rather than complex redundant systems.
Data Source
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AI summary
System and methods of increasing reliability of determined location information by using two integration filters are provided. An exemplary embodiment integrates inertial navigation system information and global navigation satellite system (GNSS) information in a real time Kalman filter (116); determines a real time location of the aircraft with the real time Kalman filter (116) based upon the INS information and the GNSS information; delays the GNSS information by an interval; integrates the INS information and the delayed GNSS information in a delay Kalman filter (118); determines a predictive location of the aircraft with the delay Kalman filter (118) based upon the INS information, the delayed GNSS information, and the interval; and in response to an inaccuracy of the real time location determined from the real time Kalman filter (116), selects the predictive location determined from the delay Kalman filter (118) as a new real time location of the aircraft.