GNSS Carrier Phase Error Modeling for Reliable State Estimation
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Solution Overview
Problem
Existing GNSS positioning systems face errors due to multipath interference, atmospheric variations, and malicious signals, leading to inaccurate state estimates and integrity risks, which are not adequately addressed by current integrity monitoring systems.
Innovation Solution
A method for processing GNSS measurements using residual error models that account for carrier phase errors with continuous derivatives, incorporating quality indicators to improve the accuracy of state information inference by modeling probability distributions more effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional GNSS measurement processing is used, then the system is simple to operate, but the state estimation accuracy deteriorates due to unmodeled carrier phase residual errors
Solution Approach 1:
The patent changes the parameter representation of carrier phase errors by introducing cyclic error models that account for the periodic nature of carrier phase measurements. The error distributions are modeled as cyclic with period equal to the carrier phase cycle, transforming the error characterization from linear to periodic parameters, which improves estimation accuracy while maintaining manageable complexity
Solution Approach 2:
The patent replaces traditional mechanical/statistical error modeling approaches with a cyclic probability distribution model specifically tailored for carrier phase measurements. This substitution introduces continuous first derivatives in the error probability density function, enabling more accurate state estimation without requiring complex hardware modifications
2Measurement precision
If residual error models with continuous derivatives are implemented, then the accuracy of state information inference is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the error modeling approach by treating carrier phase measurements differently from other GNSS measurements. Specifically, it applies cyclic error models only to carrier phase measurements where the periodic nature is critical, while other measurements can use traditional error models. This segmentation improves accuracy for the most critical measurements without applying complex models to all measurements, thus balancing accuracy and computational load
3Reliability
If cyclic residual error models are used for carrier phase measurements, then the reliability of position estimates is improved, but the integrity monitoring complexity increases
Solution Approach 1:
The patent introduces cyclic error models as an intermediary layer between the raw carrier phase measurements and the final position estimation. This intermediary model with continuous first derivatives acts as a mediator that transforms the periodic error characteristics into a form that can be more reliably processed, improving position estimate reliability while providing a structured framework for integrity monitoring
Data Source
AI summary
A method and apparatus are provided for processing GNSS measurements to infer state information. An example method includes obtaining one or more residual error models for the plurality of GNSS measurements. The one or more residual error models describe a probability distribution of errors in each of the GNSS measurements. The method further includes inferring the state information based on the one or more residual error models. The GNSS measurements include at least one carrier phase measurement. The residual error model for the at least one carrier phase measurement is cyclic, such that errors in carrier phase that are separated by an integer number of cycles are regarded as equivalent. The probability distribution for the at least one carrier phase measurement comprises a function having a continuous first derivative, for example, a continuous first derivative at a phase boundary between successive cycles.