GNSS Signal Correction Using Dynamic Hardware Delay Estimation
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Solution Overview
Problem
Existing global navigation satellite system (GNSS) correction methods are unsuitable for remote locations without a network of reference stations, as they rely on dynamic numerical value estimation and correction schemes that are not numerically stable, making accurate mobile position determination challenging.
Innovation Solution
A method using a dynamic system state model that estimates satellite-single-differenced mixed code-and-phase hardware delays from GNSS data, allowing for precise GNSS corrections to be transmitted to a mobile device, enabling accurate position determination even with a single reference station, and improving estimation accuracy by retaining original pseudo range and carrier phase observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic numerical value estimation and correction schemes are used for GNSS position determination, then position calculation can be performed, but numerical stability deteriorates and accuracy is compromised
Solution Approach 1:
The patent transforms the mathematical model by changing parameters from traditional GNSS observables (pseudorange, carrier phase) to a reparameterized system using satellite-single-differenced mixed code-and-phase hardware delays. This parameter transformation resolves the numerical instability issue while maintaining position determination capability, achieving both reliability and precision simultaneously
Solution Approach 2:
The patent segments the GNSS correction calculation into distinct components: satellite clock biases, receiver clock biases, hardware delays, and atmospheric corrections. By separating these parameters and estimating them independently through the dynamic system state model, the method achieves numerical stability while improving overall position determination accuracy
2Measurement precision
If a network of reference stations is used for GNSS corrections, then position accuracy improves, but system complexity and applicability to remote locations deteriorates
Solution Approach 1:
The patent creates a universal correction system where a single reference station can serve multiple remote users simultaneously. The dynamic system state model estimates corrections that are applicable to any user location, making the system universally applicable regardless of user distance from the reference station. This eliminates the need for multiple reference stations while maintaining accuracy
Solution Approach 2:
The patent introduces an intermediary mathematical model (the dynamic system state model) that mediates between the reference station observations and the mobile user position determination. This intermediary framework allows accurate position calculations without requiring the user to be physically close to the reference station, enhancing adaptability to remote locations
3Measurement precision
If original pseudo range and carrier phase observations are retained in the dynamic system state model, then estimation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent merges the estimation of multiple error components (satellite clock biases, receiver clock biases, hardware delays, atmospheric effects) into a single unified dynamic system state model. By combining these estimations in one framework using all original observations (pseudorange and carrier phase), the method achieves high estimation accuracy while managing computational complexity through integrated processing
Data Source
AI summary
GNS signal correction system and method for calculating GNSS corrections, and complementary mobile and mobile position determination method based on these GNSS corrections. The methods employ satellite-single-differenced mixed code-and-phase system hardware delays in modelling carrier phase system observables. These system hardware delays are dynamically estimated by the signal correction system using a dynamic system state model. The estimated system hardware delays are transmitted to the mobile, which applies these delays to a dynamic mobile state model, in order to improve the accuracy and/or convergence time of mobile position estimations.