Implementation of a C Library of Kalman Filters for Application on Embedded Systems

Schreppel, Christina; Pfeiffer, A.; Ruggaber, Julian; Brembeck, Jonathan

doi:10.3390/computers11110165

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…The research hypothesis is as follows. The Kalman filter algorithm can be used [40] to improve the measurement accuracy of distance parameter variations resulting from ultrasonic sensor output. The distance parameter is used for the calculation of the spring constant of a material.…”

Section: Object and Hypothesis Of The Studymentioning

confidence: 99%

“…The Kalman filter (KFA) algorithm is a predictor algorithm in the form of mathematical equations to estimate a process by minimizing the value of SME (Square Mean Error), and a feedback process occurs from the sensor as an output [40,[42][43][44]. The sensor output still contains noise that interferes with the expected output results.…”

Section: 2 Implementation Of Kalman Filter Algorithm To Reduce Noisementioning

confidence: 99%

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Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

Pratiwi,

Fadli,

Cahyanto

et al. 2024

EEJET

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The Kalman filter algorithm is very important as a recursive algorithm method to optimize sensor output from physical parameter measurement systems, especially physics practicum demonstration systems. One of the distance parameter measurement demonstration systems used in Hooke’s law demonstration system is applied in physics practicum, the system has problems related to fluctuating or unstable sensor output. This research implements the Kalman filter algorithm on the Arduino IDE sketch to reduce noise that appears at the ultrasonic sensor output. The methodology used in this study includes the application of the Kalman filter algorithm to the Arduino IDE sketch with the variable value of the Kalman filter algorithm equation modified with a value of R=10, H=1, and Q=1, and returns the filtered Kalman out value. The Arduino output results are exported to Ms. Excel for further analysis and generate a filtered ultrasonic sensor output signal graph compared without using the Kalman filter. The ultrasonic sensor output noise filtration effectively reduces noise by showing a decrease in the mean squared error (MSE) value and obtaining the best performance of up to 89.23 %. The accuracy of Kalman filter filtration results can be seen from the calculation that the spring constant of filtered metal materials is smaller than the conventional measurement spring constant. Accurate and effective results with the implementation of the Kalman filter algorithm can be developed for the variation values of distance parameters and Kalman filter algorithm variables (R, Q, and H) with other value variations, especially variables that produce filtering curves close to straight lines. It was concluded that the Kalman filter algorithm was able to improve the performance of Hooke’s law prop system

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Section: Object and Hypothesis Of The Studymentioning

confidence: 99%

Section: 2 Implementation Of Kalman Filter Algorithm To Reduce Noisementioning

confidence: 99%

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

Pratiwi,

Fadli,

Cahyanto

et al. 2024

EEJET

View full text Add to dashboard Cite

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Characterizing Runtime Performance Variation in Error Detection by Duplicating Instructions

Huang,

He,

et al. 2023

2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)

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Advanced Controller Development Based on eFMI with Applications to Automotive Vertical Dynamics Control

et al. 2021

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High-level modeling languages facilitate system modeling and the development of control systems. This is mainly achieved by the automated handling of differential algebraic equations which describe the dynamics of the modeled systems across different physical domains. A wide selection of model libraries provides additional support to the modeling process. Nevertheless, deployment on embedded targets poses a challenge and usually requires manual modification and reimplementation of the control system. The novel proposed eFMI Standard (Functional Mock-up Interface for embedded systems) introduces a workflow and an automated toolchain to simplify the deployment of model-based control systems on embedded targets. This contribution describes the application and verification of the eFMI workflow using a vertical dynamics control problem with an automotive application as an example. The workflow is exemplified by a control system design process which is supported by the a-causal, multi-physical, high-level modeling language Modelica. In this process, the eFMI toolchain is applied to a model-based controller for semi-active dampers and demonstrated using an eFMI-based nonlinear prediction model within a nonlinear Kalman filter. The generated code was successfully tested in different validation steps on the dedicated embedded system. Additionally, tests with a low-volume production electronic control unit (ECU) in a series-produced car demonstrated the correct execution of the controller code under real-world conditions. The novelty of our approach is that it automatically derives an embedded software solution from a high-level multi-physical model with standardized eFMI methodology and tooling. We present one of the first full application scenarios (covering all aspects ranging from multi-physical modeling up to embedded target deployment) of the new eFMI tooling.

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Implementation of a C Library of Kalman Filters for Application on Embedded Systems

Cited by 3 publications

References 27 publications

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

Characterizing Runtime Performance Variation in Error Detection by Duplicating Instructions

Advanced Controller Development Based on eFMI with Applications to Automotive Vertical Dynamics Control

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