Dual-Stage Inertial Navigation for Certified Geolocation Limits
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Solution Overview
Problem
Existing inertial navigation systems lack a reliable method to calculate geolocation data with certified accuracy, especially in critical functional chains, due to uncertainties in hybrid navigation data from non-certified sensors and external models, and fail to provide a statistical error indicator for navigation errors.
Innovation Solution
A method and unit for calculating inertial navigation data that includes a first navigation stage for hybrid navigation and a second stage for certified navigation, using a Kalman filter to estimate error covariances, ensuring certified geolocation data with a calculated error limit, and integrating a zero velocity update to maintain accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hybridation of inertial data with data from other sensors is used to improve geolocation accuracy, then measurement precision is improved, but reliability of error statistics deteriorates because error statistics cannot be reliably calculated for non-certified sensors and behavior models
Solution Approach 1:
The navigation system is segmented into two independent calculation paths: a first navigation stage that performs hybrid navigation calculations without certification, and a second navigation stage that performs certified navigation calculations with guaranteed error statistics. This segmentation allows the system to maintain high measurement precision through hybridation while ensuring reliability through the certified path that uses only sensors with known error statistics and excludes uncertified behavior models.
Solution Approach 2:
The invention introduces an intermediary certification mechanism where the second navigation stage acts as a reference frame for validating the first navigation stage. The certified geolocation data from the second stage provides a statistical guarantee that mediates between the imprecise hybrid data and the required reliability, allowing the system to use beneficial hybridation techniques while maintaining certified error bounds through comparison with the certified reference.
2Reliability
If redundant inertial units are associated to handle malfunctions, then reliability is improved, but device complexity increases and this does not address non-robust usage outside safe operating ranges
Solution Approach 1:
Instead of using static redundant inertial units, the invention implements a dynamic validation mechanism where the second navigation stage continuously monitors and validates the outputs of the first navigation stage. This dynamic approach adapts to varying operating conditions and detects non-robust usage outside safe operating ranges in real-time, providing reliability without the permanent complexity of redundant hardware units.
Solution Approach 2:
The certified navigation stage performs self-validation by comparing its results with the hybrid navigation results and using its certified error statistics to determine validity. This self-service mechanism allows the system to detect and handle malfunctions and non-robust usage without external monitoring systems or redundant units, reducing device complexity while maintaining reliability.
3Measurement precision
If GPS hybrid inertial navigation is used to define position error protection radii, then measurement precision is improved, but adaptability deteriorates because these techniques do not cover ground multipath error, deception, or use cases without GNSS signal
Solution Approach 1:
The invention creates a universal certified navigation framework in the second stage that does not depend on specific external navigation systems like GPS. By using only inertial sensors with certified error statistics and excluding uncertified behavior models, the system achieves multi-functionality that works across diverse operational environments including GNSS-denied environments, ground multipath conditions, and deception scenarios, while maintaining position error protection through statistical guarantees.
Data Source
AI summary
This inertial navigation unit comprises a navigation data acquisition unit (1) and a first navigation stage (2) capable of calculating hybrid navigation values by combining navigation data. It further includes a second navigation stage (3) capable of calculating certified navigation values independently of the hybrid navigation values, said certified navigation values being certified with a geolocation error limit.