GNSS Position Verification via Geo-Object Correlation
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Solution Overview
Problem
Existing position determination devices in vehicles face disruptions due to signal shadowing, jamming, and spoofing, leading to unreliable location positioning, especially in areas with topography-related disturbances, making it difficult to distinguish between deterministic and non-deterministic failures.
Innovation Solution
A data processing device that detects gaps in location position series by correlating predetermined geo-objects with correlation positions before and after the gap, generating an error signal based on spatial correlation and distance deviations to differentiate between topography-related and system-related GNSS disturbances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If dead reckoning is used to update location position when GNSS signals are unavailable, then position determination can continue, but measurement precision deteriorates over time and distance due to increasing measurement uncertainties
Solution Approach 1:
The system continuously monitors the plausibility of dead reckoning results by comparing them with expected travel distances and routes. When deviations exceed thresholds, the system triggers alerts or switches to alternative positioning methods, creating a feedback loop that maintains position accuracy despite using dead reckoning.
Solution Approach 2:
The system dynamically adjusts the update interval and methodology based on the duration of GNSS unavailability and the accumulated uncertainty. As dead reckoning continues, the system increases monitoring frequency and adjusts positioning strategies to compensate for growing errors.
2Duration of action of stationary object
If dead reckoning is used to determine position over longer distances, then position determination is maintained, but reliability decreases due to gap in position data series
Solution Approach 1:
The system implements continuous feedback monitoring of position data gaps by comparing sequential position data with expected travel patterns. When gaps are detected that cannot be explained by normal dead reckoning uncertainty, the system generates alerts or switches to alternative positioning methods, maintaining reliability through active monitoring.
Solution Approach 2:
The system introduces an intermediary verification layer that checks the plausibility of dead reckoning results against route databases and travel pattern models. This intermediary layer acts as a mediator between raw position data and final position output, filtering out unreliable data.
3Measurement precision
If vehicle movement sensor system is integrated to improve GNSS position, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system merges GNSS positioning with dead reckoning using vehicle movement sensors into a unified positioning solution. By combining these complementary methods, the system achieves continuous and accurate position determination, leveraging the strengths of both GNSS (when available) and dead reckoning (when GNSS is unavailable).
Solution Approach 2:
The vehicle movement sensor system serves multiple functions: it provides data for dead reckoning positioning, monitors travel patterns for anomaly detection, and validates position data plausibility. This multi-functionality justifies the added complexity by delivering comprehensive positioning and monitoring capabilities.
Data Source
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Figure 2
Figure 3a~3a'
AI summary
The invention provides a data processing device, a system, and a method for verifying the fulfillment of the intended function of a positioning device, wherein a series of location positions (pi) originating from the positioning device is characterized by a gap in which location positions are missing between a first section of a traveled path (80) and a second section of the traveled path (80), wherein an error signal is generated depending on whether there is a missing or existing correlation between predetermined geo-objects (91a, 92a, 93a, 94, 91b, 92b, 93b, 95) with the correlation positions (81, 82) representing the first and second sections.