A method for checking the correction of the system model in a Kalman filter.
The method for examining the Kalman filter's system model through stationary state identification and strapdown filter verification improves localization data accuracy and reliability, addressing sensor errors for autonomous driving systems.
JP2026524107APending Publication Date: 2026-07-17ROBERT BOSCH GMBH
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- ROBERT BOSCH GMBH
- Filing Date
- 2024-06-27
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
The accuracy and reliability of localization data provided by Kalman filters, particularly in autonomous driving applications, need improvement to meet the demands of highly automated and autonomous driving systems.
Method used
A method for examining the system model of a Kalman filter by identifying a stationary state, performing calibration during operation, and using a strapdown filter to verify the corrected parameters, thereby detecting errors and deviations due to sensor aging.
Benefits of technology
Enhances the accuracy and reliability of localization data by continuously self-calibrating the Kalman filter, addressing sensor errors and deviations, ensuring high-quality data for autonomous driving functions.
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Smart Images
Figure 2026524107000001_ABST
Abstract
The present invention relates to a method for examining the correction of a system model in a Kalman filter (1), wherein the Kalman filter (1) is part of a filter network (2) for determining localization data in an automobile, and uses a first set (3) of sensor data to determine localization data. The method includes at least the following steps: a) a step of identifying the stationary state of the vehicle during operation using a stationary state identification unit (12), thereby generating a stationary state signal (13); b) a step of performing a calibration function on the parameters of the system model in the Kalman filter (1) during the stationary state, thereby identifying a corrected parameter (5); c) a step of performing an inspection of the corrected parameter (5) by comparing the corrected parameter (5) with a comparison parameter (6) when a stationary state signal (13) is present, wherein the comparison parameter (6) is identified by another data source (10), the other data source (10) creates the comparison parameter (6) using a second set (4) of sensor data, and the second set (4) of sensor data is reduced compared to a first set (3) of sensor data; and d) a step of performing an error function (7) if the inspection performed in step c) yields a negative result.
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