Distributed Satellite Positioning Correction Network
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Solution Overview
Problem
Current GPS solutions for high precision satellite positioning are either inaccurate or require excessive processor power, and traditional Network RTK methods become computationally costly as the network size increases, limiting their effectiveness in achieving high accuracy and coverage.
Innovation Solution
A distributed dense network processing system that includes a global correction module, local correction modules, and a correction generator, which collects data from reference stations to generate both global and local correction models, allowing for flexible correction data generation and spatial interpolation, reducing convergence time and improving accuracy without the need for extensive real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Network RTK methods are used to increase network size for better coverage and accuracy, then positioning accuracy and coverage area improve, but computational cost increases non-linearly
Solution Approach 1:
The patent segments the dense network processing into two distinct components: a global correction model that processes data from all reference stations, and local correction models that process data from nearby reference stations. This segmentation allows the system to distribute computational load, where the global model handles broad corrections once, and local models handle station-specific corrections, avoiding the non-linear computational complexity of traditional Network RTK while maintaining high positioning accuracy across large coverage areas.
2Area of stationary object
If more reference stations are added to the network, then coverage area and positioning accuracy improve, but system complexity increases
Solution Approach 1:
The system segments correction generation into global and local components that can be independently managed and scaled. The global correction model provides a standardized framework that remains relatively simple regardless of network size, while local correction models are generated only when needed for specific areas. This allows the system to expand coverage by adding reference stations without proportionally increasing overall system complexity.
Solution Approach 2:
The patent performs preliminary generation of global correction models and local correction models in advance, storing them for later use. This preliminary action eliminates the need for complex real-time computations when positioning requests are made, as the correction data is pre-computed and ready for application. The system can thus handle increased coverage areas without corresponding increases in real-time processing complexity.
3Measurement precision
If real-time data from all reference stations is processed, then positioning accuracy improves, but convergence time increases
Solution Approach 1:
The patent extracts and separates global correction components from local correction components. The global correction model captures the majority of positioning errors that are common across the entire network, allowing the system to achieve high accuracy without processing all local real-time data simultaneously. This extraction reduces convergence time by focusing computational resources on the most impactful correction components first.
Solution Approach 2:
The system performs preliminary computation of global correction models using historical and real-time data from all reference stations in advance. When a positioning request is made, these pre-computed corrections are immediately applied, significantly reducing the time needed to achieve high accuracy. Local correction models are then applied as needed for finer adjustments, maintaining accuracy while minimizing convergence time.
Data Source
AI summary
A system for generating satellite positioning corrections includes a global correction module that generates a set of global pre-corrections based on modeling of global positioning error, a set of local correction modules that, for each local correction module of the set, takes input from a unique reference source and generates a set of local pre-corrections based on modeling of local positioning error; and a correction generator that generates a positioning correction from the set of global pre-corrections and the sets of local pre-corrections to correct a position of the mobile receiver.