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EP-4739979-A1 - METHOD FOR CHECKING A CORRECTION OF A SYSTEM MODEL IN A KALMAN FILTER

EP4739979A1EP 4739979 A1EP4739979 A1EP 4739979A1EP-4739979-A1

Abstract

The invention relates to a method for checking a correction of a system model in a Kalman filter (1) which is part of a filter network (2) for determining localisation data in a motor vehicle and uses, for determining localisation data, a first set (3) of sensor data, wherein the method comprises at least the following steps: a) detecting a standstill situation of the motor vehicle during operation with a standstill detector (12) such that a standstill signal (13) is generated; b) carrying out a calibration function for parameters of the system model during the standstill situation in the Kalman filter (1), wherein corrected parameters (5) are determined; c) carrying out a check of the corrected parameters (5) by comparing the corrected parameters (5) with comparison parameters (6) when a standstill signal (13) is present, wherein the comparison parameters (6) are determined using another data source (10), wherein the other data source (10) creates comparison parameters (6) using a second set (4) of sensor data, wherein the second set (4) of sensor data is smaller than the first set (3) of sensor data; d) executing an error function (7) when a check carried out in step c) is failed.

Inventors

  • Popp, Manuel
  • ROITH, Sebastian

Assignees

  • Robert Bosch GmbH

Dates

Publication Date
20260513
Application Date
20240627

Claims (13)

  1. 1. Method for checking a correction of a system model in a Kalman filter (1), which is part of a filter network (2) for determining localization data in a motor vehicle and uses a first set (3) of sensor data to determine localization data, the method comprising at least the following steps: a) detecting a standstill situation of the motor vehicle during operation with a standstill detection (12), so that a standstill signal (13) is generated; b) carrying out a calibration function for parameters of the system model during the standstill situation in the Kalman filter (1), wherein corrected parameters (5) are determined; c) carrying out a test of the corrected parameters (5) by comparing the corrected parameters (5) with comparison parameters (6) when a standstill signal (13) is present, wherein the comparison parameters (6) are determined using another data source (10), wherein the other data source (10) creates comparison parameters (6) using a second set (4) of sensor data, wherein the second set (4) of sensor data is reduced compared to the first set (3) of sensor data, d) executing an error function (7) if a test carried out in step c) is negative.
  2. 2. The method according to claim 1, wherein the first set (3) of sensor data used by the Kalman filter (1) comprises at least GNSS signals from at least one GNSS sensor (8) and inertial sensor signals from inertial sensors (9).
  3. 3. Method according to one of the preceding claims, wherein age-related changes of GNSS sensors (8) and/or inertial sensors (9) are detected by checking the corrected parameters (5) in step c).
  4. 4. Method according to one of the preceding claims, wherein data from GNSS sensors (8) are not taken into account for detecting a standstill situation in step a).
  5. 5. Method according to one of the preceding claims, wherein for the detection of a standstill situation in step a) sensor data from at least one inertial sensor (9) and/or sensor data from wheel rotation sensors (11) are used.
  6. 6. Method according to one of the preceding claims, wherein when checking the corrected parameters (5) checked in step c), at least the following parameters are checked: a covariance matrix (14) of the Kalman filter (1); an estimated state vector (x) of the Kalman filter (1); Parameters of a system model of the Kalman filter (1); and a noise vector (15) describing a system noise of the Kalman filter (1).
  7. 7. The method according to claim 6, wherein the other data source (10) is a strapdown filter provided in the filter network (2) in addition to the Kalman filter (1) (10), wherein the strapdown filter (10) uses data from at least one inertial sensor (9) to generate comparison parameters (6) for testing the corrected parameters (5) in step c).
  8. 8. The method according to claim 7, wherein the comparison parameters (6) generated with the strapdown filter (10) comprise at least one propagated state vector (y) which corresponds to the state vector (x) estimated with the Kalman filter (1) and propagates this state vector (x).
  9. 9. The method according to one of claims 6 to 8, wherein parallel to the propagation of the state vector (x) of the Kalman filter (1) with the strapdown filter (10), the at least one covariance matrix (14) of the Kalman filter (1) is propagated with the strapdown filter (10) as a propagated covariance matrix (14).
  10. 10. Method according to one of claims 6 to 9, wherein for the checking of the corrected parameters (5) according to step c) a comparison of the parameters obtained with the Kalman filter (1) determined covariance matrix (14) and the state vector (x) determined with the Kalman filter (1) with the covariance matrix (14) propagated with the strapdown filter (10) and the state vector (y) propagated with the strapdown filter (10).
  11. 11. Control device comprising a processor adapted/configured to carry out the method according to one of claims 1 to 10.
  12. 12. Computer program product comprising instructions which, when the computer program product is executed by a computer, cause the computer to carry out the method according to one of claims 1 to 10.
  13. 13. Computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of the method according to one of claims 1 to 10.

Description

Description title Method for checking a correction of a system model in a Kalman filter State of the art The invention relates to a novel method for testing a parameterization of a system model in a Kalman filter. The Kalman filter (also Kalman-Bucy filter, Stratonovich-Kalman-Bucy filter or Kalman-Bucy-Stratonovich filter) is a mathematical filter model for the iterative estimation of system states based on error-prone input data, which is in particular sensor data. The Kalman filter is used to estimate system variables that cannot be measured directly, while optimally reducing the errors in the observations. The Kalman filter usually uses the input data to maintain an internal mathematical model as a constraint, which is included in the estimation of parameters and by means of which dynamic relationships between the system variables are taken into account. For example, the mathematical model contains equations of motion with which changing positions and speeds, which are fed into the Kalman filter as input data, are related to one another, so that precise estimates can be made together based on (error-prone) input data for positions and speeds. Kalman filters are used in particular for the iterative estimation of system states on the basis of observations that are usually subject to errors. Kalman filters have proven to be particularly advantageous in this context, especially for applications in which sensor information from different sensors is to be combined (or combined) with model information. In addition, Kalman filters are often used in embedded systems because their calculations are advantageous, accurate and robust. In addition, microcontrollers can advantageously perform the calculations of a Kalman filter efficiently. Kalman filters are used in particular to fuse data from various sensors with which the positions of vehicles can be determined in order to obtain highly precise localization data. Localization data here refers in particular to position data as well as data relating to speeds and accelerations. Sensors whose data can be processed or fused using Kalman filters include GNSS sensors for determining localizations using GNSS satellites, inertial sensors and, for example, wheel sensors and steering angle sensors that are used in motor vehicles to monitor the movement of a motor vehicle via the vehicle's chassis. Such sensors thus determine a type of precursor data for localization data, which is used as input data in a Kalman filter to determine highly precise localization data. By jointly considering these described data in a Kalman filter using the system models, parameters and/or system states stored in the Kalman filter, it is possible to generate the highly precise localization data from the described input data. Kalman filters are used in particular to obtain highly precise localization data for highly automated driving functions of motor vehicles and in particular for autonomous driving functions. In a Kalman filter, the internal parameters and system states are preferably corrected permanently. The term "corrected parameters" is used here across the board for the corrections that take place in the Kalman filter. The quality of the internal parameters and/or system states in the Kalman filter are crucial for improving the localization data through the use of the Kalman filter. However, there is a fundamental need for improvement in the precision and confidence of the localization data determined using Kalman filters, because for applications of highly automated and autonomous driving, a very high level of precision and a high level of confidence in the localization data used is desirable disclosure of the invention Based on this, a particularly advantageous method for checking the design of the system model and the parameters of the system model in a Kalman filter will be described. The invention relates to a method for testing a design of a system model in a Kalman filter, which is part of a filter network for determining localization data in a motor vehicle and uses a first set of sensor data to determine localization data, the method comprising at least the following steps: a) detecting a standstill situation of the motor vehicle during operation with a standstill detection, so that a standstill signal is generated; b) carrying out a calibration function for parameters of the system model during the standstill situation in the Kalman filter, whereby corrected parameters are determined; c) carrying out a test of the corrected parameters by comparing the corrected parameters with comparison parameters if a standstill signal is present, whereby the comparison parameters are determined using another data source, whereby the other data source creates comparison parameters using a second set of sensor data, whereby the second set of sensor data is reduced compared to the first set of sensor data, d) carrying out an error function if a test carried out in step c) is negative. The bas