GNSS Navigation Using Inequality Constraints for Position Accuracy
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Solution Overview
Problem
Global Navigation Satellite Systems (GNSS) face challenges in providing accurate location estimates due to biased measurements, poor solution geometry, and low measurement redundancy, leading to erratic estimates and measurement faults.
Innovation Solution
Incorporating inequality constraints, such as altitude and speed limits, into the navigation solution using digital elevation models and user context analysis to improve position and velocity estimation, and employing these constraints as pseudo-measurements to correct measurement outliers within expected boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a recursive estimation algorithm (e.g., Kalman Filter) is used to provide a computationally efficient navigation solution, then computational efficiency is improved, but measurement precision deteriorates due to biased GNSS measurements, poor solution geometry, and low measurement redundancy
Solution Approach 1:
The patent introduces inequality constraints as intermediary elements that mediate between the GNSS measurements and the navigation solution. These constraints act as pseudo-measurements that correct biased measurements and improve solution accuracy without requiring complex computational algorithms, thus resolving the contradiction between computational efficiency and measurement precision.
Solution Approach 2:
The patent changes the parameters of the navigation solution by incorporating inequality constraints (such as altitude and speed limits) into the estimation process. This parameter change allows the system to maintain computational efficiency while improving measurement precision by using these constraints to correct biased GNSS measurements.
2Measurement precision
If inequality constraints are used as pseudo-measurements to improve position and velocity estimation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the complexity of handling measurement biases and constraints from the core navigation algorithm. By using inequality constraints as pseudo-measurements, the system separates the correction function from the estimation function, improving measurement precision while keeping the overall device complexity manageable through modular design.
3Reliability
If inequality constraints are applied to correct measurement outliers, then reliability is improved, but measurement precision may deteriorate if constraints are too restrictive
Solution Approach 1:
The patent makes the inequality constraints dynamic by adjusting them based on the current navigation context and measurement quality. This dynamic adjustment allows the system to maintain reliability through fault detection while preserving measurement precision by adapting constraints to avoid overly restrictive limitations on valid measurements.
Data Source
AI summary
Information such as altitude or speed limits for a specific geographic region can be utilized to improve position and velocity estimation for a mobile device using inequality constraints. The inequality constraints can be used as pseudo-measurements when needed to improve position and velocity estimation.