Kalman Filter for GNSS Dead Reckoning Position Error Estimation
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Solution Overview
Problem
GNSS measurements in telematics systems are inherently noisy, making it difficult to determine measurement errors without a reference position, which affects the accuracy of navigation services such as turn-by-turn directions.
Innovation Solution
A method that combines GNSS positioning information with dead reckoning (DR) navigation system data, using a Kalman filter to calculate the Estimated Horizontal Position Error (EHPE) by estimating GNSS measurement errors and DR propagation errors, allowing for more accurate navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS positioning information is used alone, then the system provides basic navigation functionality, but the measurement noise and positioning accuracy cannot be reliably determined due to environmental changes and variable satellite coverage
Solution Approach 1:
The patent combines GNSS positioning information with dead reckoning (DR) navigation system data using a Kalman filter. This merging of two independent positioning systems allows the system to cross-validate measurements and reliably determine positioning accuracy without requiring external reference positions, resolving the contradiction between measurement precision and reliability.
Solution Approach 2:
The Kalman filter implements a feedback mechanism where the system continuously estimates positioning errors based on the combination of GNSS and DR data, then uses these error estimates to improve subsequent positioning accuracy. This closed-loop feedback enables reliable error determination despite environmental variations and satellite coverage changes.
2Measurement precision
If a reference position is used to determine measurement error, then positioning accuracy can be assessed, but the system cannot operate independently without external reference infrastructure
Solution Approach 1:
The system uses self-service by employing the Kalman filter to autonomously estimate positioning errors using only its own internal sensors (GNSS and DR systems). The filter processes the combined data from both positioning systems to generate error estimates without requiring external reference positions or infrastructure, thereby maintaining system independence while achieving accurate error determination.
3Measurement precision
If Kalman filter is applied to combine GNSS and DR data, then positioning accuracy and error estimation are improved, but the device complexity and computational requirements increase
Solution Approach 1:
The Kalman filter serves multiple functions simultaneously: it fuses GNSS and DR positioning data, estimates positioning errors, and provides corrected position estimates. This multi-functionality justifies the increased computational complexity by delivering comprehensive positioning solutions that improve accuracy without requiring separate systems for each function.
Data Source
AI summary
A method is provided for determining an Estimated Horizontal Position Error (EHPE) with respect to a navigation system onboard a telematics-equipped vehicle by utilizing a Global Navigation Satellite System (GNSS) navigation system in combination with a dead reckoning (DR) navigation system. The method includes: receiving GNSS positioning information from the GNSS navigation system; receiving DR positioning information from the DR navigation system; applying, by a telematics unit, a Kalman filter to the GNSS positioning information and the DR positioning information; calculating, by the telematics unit, the EHPE corresponding to the navigation system onboard the telematics-equipped vehicle based on the GNSS positioning information, the DR positioning information, and the Kalman filter.