Kalman Filter State Estimation With Runtime Uncertainty Correction
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Solution Overview
Problem
Kalman filters are complex and require numerous setting options, making them difficult to use for new applications and maintain, especially in safety-critical areas like autonomous driving, where accurate system state estimation is crucial.
Innovation Solution
A method that estimates system states using a Kalman filter by determining discrepancies between model and measured values, attributing these discrepancies to the reliability of the estimation, and adjusting the estimation uncertainty accordingly, allowing for a direct impact on the reliability of the estimation result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Kalman filter is used to estimate system states, then measurement precision and reliability of estimation are improved, but device complexity and difficulty of operation increase due to numerous setting options
Solution Approach 1:
The filter performs self-calibration by automatically determining discrepancies between model values and measured values, and using these discrepancies to adjust its own setting options during runtime, eliminating the need for manual configuration and making the system self-optimizing
Solution Approach 2:
The filter dynamically changes its setting options (parameters) based on determined discrepancies, allowing the filter to adapt its behavior to actual system conditions rather than relying on fixed pre-configured parameters, thereby simplifying operation while maintaining precision
2Adaptability or versatility
If additional filters are added to adapt setting options automatically, then adaptability is improved, but device complexity and design effort increase
Solution Approach 1:
The adaptation function is merged directly into the Kalman filter itself rather than being implemented as separate additional filters, combining the estimation and adaptation functions into a single unified system that reduces overall complexity while maintaining adaptability
Solution Approach 2:
The filter performs self-calibration by automatically determining discrepancies between model values and measured values, and using these discrepancies to adjust its own setting options during runtime, eliminating the need for manual configuration and making the system self-optimizing
Data Source
AI summary
A method determines at least one system state of a system using a Kalman filter. At least one measured value, measured by at least one sensor of the system, is supplied to the Kalman filter. The method includes estimating the at least one system state using the Kalman filter. An estimation result and at least one associated item of information relating to a reliability of the estimation result are output. The method further includes determining a discrepancy between at least one model value associated with the estimation result and at least one measured value associated with the estimation result, and correcting the at least one associated item of information relating to the reliability of the estimation result using the determined discrepancy.