Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor

Achmad Marzuki; Taufik Muzakkir; Muhammad Sulkhan Arief

doi:doi:10.11648/j.ajece.20250902.14

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Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor

Achmad Marzuki^*

, Taufik Muzakkir

, Muhammad Sulkhan Arief

Published in American Journal of Electrical and Computer Engineering (Volume 9, Issue 2)

Received: 25 October 2025 Accepted: 13 November 2025 Published: 9 December 2025

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Abstract

The existence of a sensor is very essential and is needed by a digital system to produce the right decision. But every use of a sensor will have an error and noise process that cannot be avoided, and greatly affects the accuracy of the measurement results. One of the popular sensors in the market place is the INA219 sensor made by Texas Instrument which is often used to measure dc current. But the results of the experiment show that the sensor cannot be used directly because its output is relatively unstable or fluctuates. This paper implements the Standard Kalman Filter to reduce noise on the INA219 sensor to produce more accurate dc current measurements. The experimental results show that the variance value of the KF output is much smaller than the sensor output, so that the Kalman filter algorithm has worked optimally to produce accurate DC current measurements. Besides that, Kalman Filer is also very suitable for use in DC motor control as a dynamic system that implements duty cycle (D) changes in the PWM method on its DC input voltage. The Output of Standard Kalman Filter which has been implemented with the ESP32 are influenced by the values assigned to the noise sensor covariance matrix (R) and process noise covariance (Q). With a value of R=100, for resistive loads the values of Q=0.5, while for inductive load (DC motor), the values of Q=0.05 have effectively reduced the amount of noise to enhance the accuracy and precision in using the INA219 current sensor.

Published in	American Journal of Electrical and Computer Engineering (Volume 9, Issue 2)
DOI	10.11648/j.ajece.20250902.14
Page(s)	45-55
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Sensor, INA219, Kalman Filter, Esp32, Accuracy, Precision

References

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APA Style

Marzuki, A., Muzakkir, T., Arief, M. S. (2025). Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor. American Journal of Electrical and Computer Engineering, 9(2), 45-55. https://doi.org/10.11648/j.ajece.20250902.14

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ACS Style

Marzuki, A.; Muzakkir, T.; Arief, M. S. Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor. Am. J. Electr. Comput. Eng. 2025, 9(2), 45-55. doi: 10.11648/j.ajece.20250902.14

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AMA Style

Marzuki A, Muzakkir T, Arief MS. Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor. Am J Electr Comput Eng. 2025;9(2):45-55. doi: 10.11648/j.ajece.20250902.14

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@article{10.11648/j.ajece.20250902.14,
  author = {Achmad Marzuki and Taufik Muzakkir and Muhammad Sulkhan Arief},
  title = {Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {9},
  number = {2},
  pages = {45-55},
  doi = {10.11648/j.ajece.20250902.14},
  url = {https://doi.org/10.11648/j.ajece.20250902.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20250902.14},
  abstract = {The existence of a sensor is very essential and is needed by a digital system to produce the right decision. But every use of a sensor will have an error and noise process that cannot be avoided, and greatly affects the accuracy of the measurement results. One of the popular sensors in the market place is the INA219 sensor made by Texas Instrument which is often used to measure dc current. But the results of the experiment show that the sensor cannot be used directly because its output is relatively unstable or fluctuates. This paper implements the Standard Kalman Filter to reduce noise on the INA219 sensor to produce more accurate dc current measurements. The experimental results show that the variance value of the KF output is much smaller than the sensor output, so that the Kalman filter algorithm has worked optimally to produce accurate DC current measurements. Besides that, Kalman Filer is also very suitable for use in DC motor control as a dynamic system that implements duty cycle (D) changes in the PWM method on its DC input voltage. The Output of Standard Kalman Filter which has been implemented with the ESP32 are influenced by the values assigned to the noise sensor covariance matrix (R) and process noise covariance (Q). With a value of R=100, for resistive loads the values of Q=0.5, while for inductive load (DC motor), the values of Q=0.05 have effectively reduced the amount of noise to enhance the accuracy and precision in using the INA219 current sensor.},
 year = {2025}
}

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TY  - JOUR
T1  - Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor
AU  - Achmad Marzuki
AU  - Taufik Muzakkir
AU  - Muhammad Sulkhan Arief
Y1  - 2025/12/09
PY  - 2025
N1  - https://doi.org/10.11648/j.ajece.20250902.14
DO  - 10.11648/j.ajece.20250902.14
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 45
EP  - 55
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20250902.14
AB  - The existence of a sensor is very essential and is needed by a digital system to produce the right decision. But every use of a sensor will have an error and noise process that cannot be avoided, and greatly affects the accuracy of the measurement results. One of the popular sensors in the market place is the INA219 sensor made by Texas Instrument which is often used to measure dc current. But the results of the experiment show that the sensor cannot be used directly because its output is relatively unstable or fluctuates. This paper implements the Standard Kalman Filter to reduce noise on the INA219 sensor to produce more accurate dc current measurements. The experimental results show that the variance value of the KF output is much smaller than the sensor output, so that the Kalman filter algorithm has worked optimally to produce accurate DC current measurements. Besides that, Kalman Filer is also very suitable for use in DC motor control as a dynamic system that implements duty cycle (D) changes in the PWM method on its DC input voltage. The Output of Standard Kalman Filter which has been implemented with the ESP32 are influenced by the values assigned to the noise sensor covariance matrix (R) and process noise covariance (Q). With a value of R=100, for resistive loads the values of Q=0.5, while for inductive load (DC motor), the values of Q=0.05 have effectively reduced the amount of noise to enhance the accuracy and precision in using the INA219 current sensor.
VL  - 9
IS  - 2
ER  -

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Author Information

Achmad Marzuki

Department of Electrical Engineering, Pontianak State Polytechnic, Pontianak, Indonesia

Contact Email

http://orcid.org/0000-0002-4290-111X
Taufik Muzakkir

Department of Electrical Engineering, Pontianak State Polytechnic, Pontianak, Indonesia

Contact Email

http://orcid.org/0009-0000-3926-5880
Muhammad Sulkhan Arief

Department of Electrical Engineering, Pontianak State Polytechnic, Pontianak, Indonesia

Contact Email

http://orcid.org/0009-0007-7613-0484

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APA Style

Marzuki, A., Muzakkir, T., Arief, M. S. (2025). Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor. American Journal of Electrical and Computer Engineering, 9(2), 45-55. https://doi.org/10.11648/j.ajece.20250902.14

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ACS Style

Marzuki, A.; Muzakkir, T.; Arief, M. S. Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor. Am. J. Electr. Comput. Eng. 2025, 9(2), 45-55. doi: 10.11648/j.ajece.20250902.14

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AMA Style

Marzuki A, Muzakkir T, Arief MS. Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor. Am J Electr Comput Eng. 2025;9(2):45-55. doi: 10.11648/j.ajece.20250902.14

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@article{10.11648/j.ajece.20250902.14,
  author = {Achmad Marzuki and Taufik Muzakkir and Muhammad Sulkhan Arief},
  title = {Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {9},
  number = {2},
  pages = {45-55},
  doi = {10.11648/j.ajece.20250902.14},
  url = {https://doi.org/10.11648/j.ajece.20250902.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20250902.14},
  abstract = {The existence of a sensor is very essential and is needed by a digital system to produce the right decision. But every use of a sensor will have an error and noise process that cannot be avoided, and greatly affects the accuracy of the measurement results. One of the popular sensors in the market place is the INA219 sensor made by Texas Instrument which is often used to measure dc current. But the results of the experiment show that the sensor cannot be used directly because its output is relatively unstable or fluctuates. This paper implements the Standard Kalman Filter to reduce noise on the INA219 sensor to produce more accurate dc current measurements. The experimental results show that the variance value of the KF output is much smaller than the sensor output, so that the Kalman filter algorithm has worked optimally to produce accurate DC current measurements. Besides that, Kalman Filer is also very suitable for use in DC motor control as a dynamic system that implements duty cycle (D) changes in the PWM method on its DC input voltage. The Output of Standard Kalman Filter which has been implemented with the ESP32 are influenced by the values assigned to the noise sensor covariance matrix (R) and process noise covariance (Q). With a value of R=100, for resistive loads the values of Q=0.5, while for inductive load (DC motor), the values of Q=0.05 have effectively reduced the amount of noise to enhance the accuracy and precision in using the INA219 current sensor.},
 year = {2025}
}

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TY  - JOUR
T1  - Implementation of a Kalman Filter for Noise Reduction on the INA219 Current Sensor
AU  - Achmad Marzuki
AU  - Taufik Muzakkir
AU  - Muhammad Sulkhan Arief
Y1  - 2025/12/09
PY  - 2025
N1  - https://doi.org/10.11648/j.ajece.20250902.14
DO  - 10.11648/j.ajece.20250902.14
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 45
EP  - 55
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20250902.14
AB  - The existence of a sensor is very essential and is needed by a digital system to produce the right decision. But every use of a sensor will have an error and noise process that cannot be avoided, and greatly affects the accuracy of the measurement results. One of the popular sensors in the market place is the INA219 sensor made by Texas Instrument which is often used to measure dc current. But the results of the experiment show that the sensor cannot be used directly because its output is relatively unstable or fluctuates. This paper implements the Standard Kalman Filter to reduce noise on the INA219 sensor to produce more accurate dc current measurements. The experimental results show that the variance value of the KF output is much smaller than the sensor output, so that the Kalman filter algorithm has worked optimally to produce accurate DC current measurements. Besides that, Kalman Filer is also very suitable for use in DC motor control as a dynamic system that implements duty cycle (D) changes in the PWM method on its DC input voltage. The Output of Standard Kalman Filter which has been implemented with the ESP32 are influenced by the values assigned to the noise sensor covariance matrix (R) and process noise covariance (Q). With a value of R=100, for resistive loads the values of Q=0.5, while for inductive load (DC motor), the values of Q=0.05 have effectively reduced the amount of noise to enhance the accuracy and precision in using the INA219 current sensor.
VL  - 9
IS  - 2
ER  -

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