Kalman Filter Navigation Integrity Against GNSS Errors
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Solution Overview
Problem
Current INS/GNSS hybridization techniques are not optimal for protecting against GNSS errors such as satellite failures, software or hardware failures, or intentional or unintentional interference, and do not provide integrated positioning when such errors occur.
Innovation Solution
A navigation and positioning device with a closed-loop primary Kalman filter and a bank of N secondary Kalman filters that periodically reconfigure themselves to compute corrections from non-satellite data, checking the integrity of GNSS measurements by comparing filter states and raising an alarm if differences exceed a threshold, ensuring the device operates independently of GNSS vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If INS/GNSS hybridization is implemented using current techniques, then positioning accuracy is improved, but vulnerability to GNSS errors and failures increases
Solution Approach 1:
The system segments the positioning computation into multiple independent Kalman filters (primary and secondary) that process GNSS and non-GNSS data separately. This segmentation allows the system to maintain accurate positioning through the primary filter while using secondary filters as independent verification mechanisms to detect and protect against GNSS errors, thus resolving the contradiction between accuracy and reliability.
Solution Approach 2:
The secondary Kalman filters act as intermediary verification mechanisms between the primary positioning system and potential GNSS errors. These filters process only non-GNSS data and serve as a mediating layer to detect inconsistencies in GNSS measurements, enabling the system to maintain reliability without sacrificing positioning accuracy from the primary filter.
2Reliability
If a bank of secondary Kalman filters is added to verify GNSS integrity, then protection against GNSS errors is improved, but device complexity increases
Solution Approach 1:
The secondary Kalman filters are designed with multi-functionality: they serve as backup positioning systems, integrity verification mechanisms, and automatic switching triggers. This universal design allows the system to achieve comprehensive GNSS error protection without proportionally increasing complexity, as each filter performs multiple protective functions simultaneously.
Solution Approach 2:
The system manages complexity by dynamically changing operational parameters: the secondary filters are activated only when GNSS integrity is questioned, and the system transitions between GNSS-dependent and GNSS-independent modes based on detected error conditions. This parameter-based control allows integrity verification without permanently maintaining complex dual architectures.
3Reliability
If secondary Kalman filters compute corrections solely from non-satellite data, then independence from GNSS vulnerabilities is improved, but long-term positioning drift increases
Solution Approach 1:
The system employs periodic action by having secondary Kalman filters reconfigure themselves periodically on the primary filter at intervals of N×T. This periodic reconfiguration allows the secondary filters to maintain independence from GNSS vulnerabilities while regularly resetting their drift through synchronization with the primary filter, thus balancing reliability and precision over time.
Solution Approach 2:
The closed-loop architecture provides feedback where secondary filter outputs are compared with primary filter outputs to detect discrepancies. When drift or errors are detected, the system uses this feedback to trigger reconfiguration events, allowing the secondary filters to maintain their drift-free characteristics while periodically realigning with the primary system to prevent long-term drift accumulation.
Data Source
AI summary
A navigation and positioning device including an inertial measurement unit, a receiver for receiving GNSS satellite positioning measurements, a closed-loop primary Kalman filter configured to compute navigation data corrections by hybridizing satellite data and non-satellite data, as well as a bank of N closed-loop secondary Kalman filters, each configured to compute navigation data corrections based solely on the non-satellite positioning data delivered at least by the inertial measurement unit, each secondary Kalman filter of index i, where 1≤i≤N, being able to reconfigure itself to the primary Kalman filter at a time (i−1)T from the start of navigation of the vehicle, and then periodically at a period N×T, T being a predetermined duration.