Satellite Receiver Vector Tracking Kalman Filter
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Solution Overview
Problem
Current GPS systems face limitations in positioning accuracy due to clock biases, atmospheric signal interactions, and errors from satellite geometry changes, particularly in critical applications like aeronautics, where precise and robust positioning is essential.
Innovation Solution
A satellite positioning receiver employing an extended Kalman filter for vector tracking, utilizing phase measurements and inertial data to improve positioning accuracy and robustness, while also calculating a protection radius to ensure integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct pseudo-distance measurement is used, then the positioning system is simple to operate, but positioning precision is insufficient for critical applications
Solution Approach 1:
The patent combines direct pseudo-distance measurement with differential correction measurements into a unified measurement set that is processed by the extended Kalman filter. This merging allows the system to achieve high precision (comparing code phase and carrier phase measurements) while maintaining operational simplicity through automated processing.
Solution Approach 2:
The extended Kalman filter acts as an intermediary that processes multiple measurement types (code phase, carrier phase, differential corrections) and produces a unified high-precision position estimate. This intermediary component handles the complexity of combining different measurement sources, allowing the overall system to achieve high precision without requiring the end user to manually manage the complexity.
2Measurement precision
If differential measurement is used to improve precision, then positioning accuracy improves, but errors from reflections and thermal noise remain
Solution Approach 1:
The extended Kalman filter uses feedback from multiple measurement sources (code phase, carrier phase, differential corrections) to continuously estimate and correct position, velocity, and clock bias. This feedback mechanism allows the system to maintain high accuracy while compensating for errors from reflections and thermal noise through statistical processing of redundant measurements.
Solution Approach 2:
The patent uses a composite measurement approach, combining multiple types of measurements (code phase pseudo-distance, carrier phase pseudo-distance, differential corrections) into a unified estimation process. This composite measurement strategy improves both accuracy and reliability by leveraging the strengths of different measurement types and using statistical filtering to reduce the impact of individual measurement errors.
3Reliability
If vector tracking with extended Kalman filter is implemented, then robustness against satellite geometry changes improves, but device complexity increases
Solution Approach 1:
The extended Kalman filter serves multiple functions simultaneously: it estimates position, velocity, clock bias, ionospheric delay, and processes multiple satellite signals. This multi-functionality provides robustness against satellite geometry changes without requiring separate processing systems for each function, thereby managing complexity through a unified approach.
4Measurement precision
If code phase measurement is used, then measurement is simple, but measurement precision is noisy
Solution Approach 1:
The patent merges code phase measurements with carrier phase measurements and differential corrections in the extended Kalman filter processing. This combination allows the system to achieve high precision by leveraging the low noise characteristics of carrier phase measurements while maintaining the simplicity of code phase acquisition, with the filter automatically weighting each measurement type according to its reliability.
Data Source
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AI summary
The receiver has an extended Kalman filter (FK) carrying out propagation of a vector of an estimated state including an error position and implementing a matrix propagation equation utilizing a propagation matrix. The filter performs a resetting on a basis of a phase error and a code error directly received from phase and code discriminators (DISCR-P-C) of a channel (CANAL-i). A calculating unit (MC1-CDE) calculates code-wise and carrier-wise control signals for code phase and carrier phase numerically-controlled oscillators for the channel based on data provided by the filter.