Robert bosch gmbh (20240134063). Method for Determining at Least One System State by Means of a Kalman Filter simplified abstract

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Method for Determining at Least One System State by Means of a Kalman Filter

Organization Name

robert bosch gmbh

Inventor(s)

Holger Digel of Kusterdingen (DE)

Simon Weissenmayer of Flein (DE)

Method for Determining at Least One System State by Means of a Kalman Filter - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240134063 titled 'Method for Determining at Least One System State by Means of a Kalman Filter

Simplified Explanation

The abstract describes a method for determining at least one system state using a Kalman filter assembly, where measured values from sensors are used to estimate the system state. The method involves performing two estimations using different Kalman filters with varying settings, and then fusing the results to produce an overall estimation result for the system state.

  • Explanation of the patent:
 * The method involves using a first Kalman filter to estimate the system state and outputting the estimation result along with information about its reliability.
 * A second Kalman filter with different settings is then used to perform a second estimation of the system state, and its result is also output along with reliability information.
 * The results from the first and second estimations are fused together to produce an overall estimation result for the system state, taking into account the reliability information from both estimations.

Potential Applications

This technology could be applied in various fields such as:

  • Robotics
  • Autonomous vehicles
  • Aerospace industry
  • Industrial automation

Problems Solved

  • Improved accuracy in estimating system states
  • Enhanced reliability of estimation results
  • Better performance in dynamic systems

Benefits

  • Increased precision in system state estimation
  • Enhanced system performance
  • Real-time monitoring and control capabilities

Potential Commercial Applications

  • Automotive industry for self-driving cars
  • Manufacturing industry for process control
  • Aerospace industry for flight control systems
  • Robotics industry for autonomous robots

Possible Prior Art

One possible prior art could be the use of Kalman filters in estimation and control systems, but the specific method described in the patent application, involving the fusion of results from multiple Kalman filters with different settings, may be a novel approach.

Unanswered Questions

How does this method compare to other fusion techniques for estimation results?

This article does not provide a comparison with other fusion techniques for estimation results. It would be interesting to see how this method stacks up against other approaches in terms of accuracy and reliability.

What are the computational requirements for implementing this method in real-time systems?

The article does not discuss the computational requirements for implementing this method in real-time systems. Understanding the computational overhead of this approach would be crucial for practical applications.


Original Abstract Submitted

a method for determining at least one system state by way of a kalman filter assembly, wherein at least one measured value measured by at least one sensor of the system is supplied to the kalman filter assembly is disclosed. the method includes (a) performing a first estimation of the system state by way of a first kalman filter of the kalman filter assembly, a first estimation result and at least one associated first item of information about the reliability of the first estimation result being output, (b) performing a second estimation of the system state by way of a second kalman filter of the kalman filter assembly, a second estimation result and at least one associated second item of information about the reliability of the second estimation result being output, the second kalman filter differing from the first kalman filter in at least one setting parameter, and (c) fusing the first estimation result and the second estimation result to produce an overall estimation result for the system state, and fusing the first item of information about the reliability of the first estimation result and the second item of information about the reliability of the second estimation result to produce an overall item of information about the reliability of the overall estimation result.

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