GNSS Ambiguity Resolution Using Informative Measurement Subsets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing GNSS systems face challenges in resolving integer ambiguities due to loss-of-lock, multipath errors, and distance-dependent biases, leading to increased computational complexity and reduced accuracy in positioning, especially in urban environments and long receiver separations.
Innovation Solution
A method using a Kalman filter-based system with a motion model and measurement model to select a subset of measurements from the measurement matrix, optimizing the selection based on informational value to reduce computational complexity while maintaining accuracy, by employing non-integer combinations of satellite measurements and utilizing the Fisher information matrix to project measurements into a lower-dimensional subspace.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all GNSS measurements are used for position estimation, then positioning accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent extracts only the most informative measurements from the complete set of GNSS measurements. By using Fisher information matrix to identify and select a reduced subset of measurements that contain the essential positioning information, the system achieves accurate position estimation with significantly lower computational complexity, resolving the contradiction between using all measurements for accuracy versus using fewer measurements for computational efficiency
Solution Approach 2:
The patent segments the full measurement matrix into a reduced subset by applying Fisher information matrix analysis. This segmentation process identifies which measurements are most critical for positioning accuracy and separates them from redundant or less informative measurements, enabling the system to process only the essential data while maintaining positioning precision
2Reliability
If integer ambiguities are re-determined after loss-of-lock, then positioning reliability is maintained, but resolution time increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing Fisher information matrix values and measurement selection criteria before ambiguity resolution is needed. When loss-of-lock occurs and ambiguity re-determination is required, the system can immediately apply pre-identified informative measurements, significantly reducing the time needed to resolve ambiguities while maintaining reliability
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors measurement quality and reliability. When loss-of-lock or multipath errors are detected, the feedback loop triggers ambiguity re-determination using the optimized measurement subset, and the results are fed back to update the positioning solution, ensuring reliable operation with minimized resolution time
3Measurement precision
If measurements from multiple satellites are combined, then positioning accuracy is improved, but distance-dependent biases increase
Solution Approach 1:
The patent applies local quality by evaluating and selecting measurements based on their specific characteristics and reliability. Using Fisher information matrix, the system identifies which satellite measurements are most informative and least affected by distance-dependent biases for each specific positioning scenario, rather than uniformly treating all measurements equally, thereby maintaining accuracy while minimizing bias effects
Solution Approach 2:
The patent changes the parameters of measurement selection by dynamically adjusting which measurements are used based on signal quality, satellite geometry, and environmental conditions. The Fisher information matrix analysis allows the system to adapt the measurement subset to current operating conditions, optimizing the balance between using multiple satellites for accuracy and avoiding distance-dependent biases
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A system for tracking a state of a GNSS receiver uses a subset of the measurements of satellite signals selected to minimize a loss of information with respect to the set of measurements available to the GNSS receiver. The system uses a probabilistic state estimator that tracks the state of the GNSS receiver using a probabilistic motion model subject to noise and a probabilistic measurement model relating the selected subset of the measurements of satellite signals to the current state of the receiver.