Urban GNSS Positioning With Ground Sensor Error Correction
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Solution Overview
Problem
Existing GNSS positioning systems struggle to achieve high accuracy in urban environments due to signal propagation errors caused by the atmosphere and close vicinity obstacles, which are not effectively addressed by current methods like RTK and PPP, particularly affecting applications such as autonomous vehicles and V2V/V2I communication.
Innovation Solution
A system and method that uses ground-based sensors mounted on rooftops and street levels to correct satellite and atmospheric errors, selecting a best set of visible satellites for GNSS receivers, combining raw measurements and best-visible satellite sets to compensate for measurement errors, and implementing RTK solutions that scale with multiple mobile receivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RTK and PPP methods are used to correct satellite and atmospheric errors, then positioning accuracy is improved, but they fail to effectively address close vicinity obstacle errors in urban environments
Solution Approach 1:
The patent segments the error sources into three distinct categories: satellite errors, atmospheric errors, and close vicinity obstacle errors. By treating each error source separately and developing specific correction strategies for each segment, the system can effectively address all types of errors including those caused by urban obstacles that traditional methods miss.
Solution Approach 2:
The patent introduces ground-based sensors as intermediary devices mounted on rooftops and street-level structures. These sensors act as mediators between the satellites and mobile receivers, providing reference measurements that enable correction of close vicinity obstacle errors. The sensors capture multipath and NLOS conditions locally and transmit correction data to mobile receivers in real-time.
2Measurement precision
If ground-based sensors are deployed on rooftops and street levels to correct all error types, then positioning accuracy reaches centimeter-level precision, but system complexity increases
Solution Approach 1:
The ground-based sensors are designed to perform multiple functions: they measure satellite signals, detect multipath conditions, identify NLOS scenarios, and provide reference data for correction. This multi-functionality reduces the need for separate specialized devices for each error type, thereby managing system complexity while achieving comprehensive error correction.
Solution Approach 2:
The system uses the existing urban infrastructure (rooftops, street-level structures) to mount sensors, rather than requiring dedicated correction towers or specialized infrastructure. The sensors leverage the natural urban environment for deployment, reducing infrastructure complexity. Additionally, the system self-calibrates by comparing measurements from multiple sensors to automatically identify and correct error patterns.
3Reliability
If multiple ground-based sensors are deployed across urban areas to provide comprehensive coverage, then correction effectiveness is improved, but deployment cost and infrastructure requirements increase
Solution Approach 1:
The patent combines multiple error correction functions into a single integrated sensor platform. The ground-based sensors simultaneously perform satellite signal measurement, multipath detection, and NLOS identification, eliminating the need for separate specialized devices. This merging reduces deployment cost and simplifies infrastructure requirements while maintaining comprehensive correction effectiveness.
Solution Approach 2:
The system dynamically adjusts sensor deployment parameters such as measurement frequency, correction data transmission intervals, and sensor activation based on urban environment characteristics. In areas with severe obstruction, sensors are activated more frequently and with higher measurement rates, while in open areas, parameters are reduced. This adaptive parameter adjustment optimizes correction effectiveness while minimizing deployment and operational costs.
Data Source
AI summary
A system and method for providing an accurate position for a GNSS device in an urban environment. The method includes determining a correction model based on differencing data and visibility data received from a plurality of sensors, estimating a current location of the GNSS device, deriving satellite parameters of a set of best visible satellites based at least on the determined correction model and the estimated current location, determining an accurate position of the GNSS device based on derived satellite parameters of the set of best visible satellites and a current location measurement provided by a GNSS receiver in the GNSS device, and setting a location of the GNSS device based on the accurate position.