GNSS Relative Positioning via Kalman Filter Ambiguity Resolution
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Solution Overview
Problem
Single-frequency GNSS receivers face accuracy issues due to ambiguity in carrier phase measurements, which cannot be compensated like dual-frequency receivers, limiting their relative positioning accuracy.
Innovation Solution
Implementing time domain recursive filtering, specifically using a Kalman filter to process single-frequency measurements, forming double differences and estimating relative position by resolving float ambiguities, thereby achieving accurate relative positioning similar to dual-frequency receivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-frequency GNSS receivers use carrier phase measurements for relative positioning, then positioning capability is provided, but measurement ambiguity reduces positioning accuracy
Solution Approach 1:
The patent applies feedback by using the estimated relative position from previous time epochs to inform and constrain the current position estimation. The Kalman filter uses the state estimate from the previous epoch as prior information, creating a feedback loop that progressively refines the position estimate and resolves carrier phase ambiguities over time, transforming the unreliable ambiguous measurement into reliable positioning information.
Solution Approach 2:
The patent performs preliminary action by pre-processing the carrier phase measurements to form double differences between receivers, which eliminates satellite clock errors and reduces ionospheric effects before the actual position estimation. This preliminary processing prepares the measurements in a form that is more suitable for accurate relative positioning and ambiguity resolution.
2Measurement precision
If dual-frequency GNSS receivers are used to compensate for carrier phase ambiguity, then positioning accuracy is improved, but receiver cost and complexity increase
Solution Approach 1:
The patent creates a virtual dual-frequency effect by forming double difference measurements between two single-frequency receivers. This copying approach replicates the ambiguity-resolution capability of dual-frequency systems using only single-frequency measurements combined with time-domain filtering, achieving similar positioning accuracy without requiring expensive dual-frequency hardware.
Solution Approach 2:
The patent introduces time domain filtering (Kalman filter) as an intermediary processing step between the single-frequency carrier phase measurements and the final position estimate. This intermediary filter acts as a mathematical dual-frequency component, providing the additional constraints needed to resolve carrier phase ambiguities without requiring actual dual-frequency hardware measurements.
3Measurement precision
If time domain recursive filtering is applied to single-frequency measurements, then positioning accuracy improves over time, but processing complexity increases
Solution Approach 1:
The patent applies dynamics by using the Kalman filter, which is a dynamic estimation algorithm that adapts to changing conditions over time. The filter continuously updates the position estimate based on new measurements and the system's dynamic model, allowing the solution to evolve and improve accuracy as more measurements are processed, while efficiently managing computational complexity through recursive updates.
Data Source
AI summary
The present invention relates to a method and apparatus for estimating relative position in a global navigation satellite system. In one embodiment, a method for estimating a position of a first receiver relative to a second receiver in a global navigation satellite system includes obtaining a first set of measurements from the first receiver, where the first set of measurements estimates a first distance between the first receiver and a satellite in the global navigation satellite system, obtaining a second set of measurements from the second receiver, where the second set of measurements estimates a second distance between the second receiver and a satellite in the global navigation satellite system, performing time domain recursive filtering in accordance with the first set of measurements and the second set of measurements, and estimating the position of a first receiver relative to a second receiver in accordance with the time domain recursive filtering.