GNSS Cluster Positioning Without Reference Receiver
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Solution Overview
Problem
Current navigation systems, such as GPS, face challenges in achieving precise position determination due to satellite errors, radio propagation errors, and receiver errors, especially in areas with poor satellite signal coverage, and often require a fixed reference receiver or correction data from public services.
Innovation Solution
A method and system that utilize a cluster of mobile devices with GNSS receivers to determine timing data from multiple satellites, communicate these measurements to a position processor, and solve simultaneous equations to correct for clock offsets and common space segment errors, allowing for accurate position determination without a fixed reference point, using relative positioning systems and additional technologies like UWB or inertial navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed reference receiver is used to correct satellite and radio link errors, then measurement precision is improved, but device complexity increases and the system requires additional infrastructure
Solution Approach 1:
The system allows mobile devices to self-correct their positioning measurements by using relative positioning algorithms. Each device in the cluster uses measurements from other devices and satellites to compute correction terms locally, eliminating the need for external reference receivers or correction services. The devices serve themselves by leveraging the collective data from the cluster.
Solution Approach 2:
The system combines measurements from multiple mobile devices and multiple satellites to collectively determine and correct space segment errors. By merging data from m devices observing n satellites, the system creates a distributed reference network that achieves differential GPS accuracy without requiring a fixed reference receiver.
2Measurement precision
If differential GPS techniques are used to correct satellite and radio link errors, then measurement precision is improved, but the system requires correction data from public services or locally installed reference receivers
Solution Approach 1:
Mobile devices independently compute their own correction terms using relative positioning algorithms. Each device processes timing data from satellites and other devices to determine clock offsets and space segment errors locally, without requiring correction data from external public services or infrastructure.
Solution Approach 2:
Instead of using a fixed reference receiver to generate correction data for all users, the system inverts the approach by having each mobile device generate its own corrections based on relative measurements from other mobile devices. The correction flow goes from the distributed mobile devices themselves rather than from a centralized reference source.
3Ease of operation
If standard GPS positioning is used, then ease of operation is maintained, but measurement precision deteriorates due to satellite errors, radio propagation errors, and receiver errors
Solution Approach 1:
The system maintains operational simplicity by allowing mobile devices to automatically perform relative positioning calculations without user intervention. The correction process happens transparently in the background as devices exchange timing data and compute positions, preserving ease of use while dramatically improving accuracy.
Solution Approach 2:
The system implements feedback by having mobile devices continuously exchange timing measurements and use these to iteratively refine position estimates and correct for satellite and atmospheric errors. The relative positioning algorithm uses feedback from multiple devices to converge on accurate positions despite initial measurement errors.
4Adaptability or versatility
If a cluster of mobile devices performs relative positioning measurements, then adaptability is improved for areas with poor satellite coverage, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system merges computational resources across the cluster of mobile devices. Each device contributes its satellite measurements and timing data to the collective positioning solution, distributing the computational workload. By combining measurements from m devices observing n satellites, the cluster achieves robust positioning in poor coverage areas while sharing processing requirements.
Solution Approach 2:
Each mobile device in the cluster serves multiple functions: it acts as both a positioning receiver and a reference source for other devices. The devices universally perform both measurement collection and correction generation, eliminating the need for specialized reference receivers and enabling operation in diverse environments including areas with poor satellite coverage.
Data Source
AI summary
A method is provided whereby a group of receivers local to one another make GNSS measurements of a number of satellites, from one or multiple different GNSS's, and by combining the measurements of the same satellites made by the different receivers it is possible given sufficient conditions to determine the positions of the receivers and the satellite errors such that the positions obtained are equivalent to those obtained from a traditional differential GNSS system, without the need for a static reference receiver at a known location.