RTK Filter Backup Data for Rapid Position Recovery
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Solution Overview
Problem
Current satellite navigation systems, such as GNSS, face significant downtime due to temporary signal losses, requiring lengthy convergence periods to regain precise positioning accuracy, leading to wasted operational time in applications like heavy equipment operation and construction.
Innovation Solution
A method utilizing backup data and real-time kinematic filtering to rapidly recover precise position estimates by storing ambiguity solutions and using them to quickly re-converge after signal interruptions, leveraging wide-lane and narrow-lane ambiguity resolution and ionospheric bias estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If PPP correction data is used for global positioning, then regional base station infrastructure is eliminated, but convergence time increases beyond RTK methods
Solution Approach 1:
The system performs preliminary actions by storing backup data including resolved ambiguities, position estimates, and measurement corrections at regular intervals before signal loss occurs. When signal interruption happens, this pre-stored data enables rapid recovery without restarting the full convergence process, thus maintaining global positioning capability while reducing convergence time after interruptions
Solution Approach 2:
The system changes the state of ambiguity resolution by implementing a two-stage process: first resolving wide-lane ambiguities quickly, then narrow-lane ambiguities. This parameter-based staged approach allows the system to maintain global validity while accelerating convergence compared to traditional PPP methods that resolve all ambiguities simultaneously or sequentially without optimization
2Measurement precision
If signal loss occurs in PPP system, then positioning accuracy is lost, but full convergence period must be restarted
Solution Approach 1:
The system performs preliminary actions by continuously storing backup data with resolved ambiguities and position estimates at regular intervals. When signal loss occurs, the system retrieves the most recent valid backup data and applies it immediately upon signal recovery, avoiding the need to restart the full convergence process and minimizing loss of positioning accuracy over time
Solution Approach 2:
The system discards the corrupted or outdated data during signal interruption and recovers by retrieving previously stored valid backup data. This selective discarding and recovery of data states allows rapid restoration of positioning accuracy without being constrained by the duration of the signal loss event
3Measurement precision
If RTK base stations are deployed for local corrections, then positioning accuracy is maintained, but infrastructure investment and communication requirements increase
Solution Approach 1:
The system implements self-service by using its own previously collected and processed data (backup data with resolved ambiguities) to correct and accelerate its positioning solution after signal interruptions. This eliminates the need for external base station infrastructure and continuous communication links, maintaining positioning accuracy through autonomous use of stored correction data
Solution Approach 2:
The system achieves universality by creating a solution that works both as a standalone PPP system and incorporates RTK-like rapid convergence capabilities without requiring physical base stations. The backup data mechanism provides correction functionality that is universally applicable regardless of location or infrastructure availability
Data Source
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AI summary
A real-time kinematic (RTK) filter (48) uses the backup data to estimate a relative position vector between the mobile receiver (20) at the first measurement time and the mobile receiver (20) at the second measurement time and to provide recovery data associated with a satellite-differenced double-difference estimation for the mobile receiver (20) between the first measurement time and the second measurement time. A navigation positioning estimator (50) can apply the relative position vector, the backup data, the recovery data from the RTK filter (48), and received correction data with precise clock and orbit information on the satellite signals, as inputs, constraints, or both for convergence or resolution of wide-lane and narrow-lane ambiguities, and determination of a precise position, in accordance with a precise positioning algorithm.