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Dergiler / Kocaeli Journal of Science and Engineering / 2026 / Cilt: 8 Sayı: 1

Microcontroller-Based Kalman Filter Measurement of Ambient Temperature

Mustafa Çelik(KOCAELI UNIVERSITY, FACULTY OF ENGINEERING)0000-0001-8397-4377
Tarık Erfidan(KOCAELI UNIVERSITY, FACULTY OF ENGINEERING)0000-0001-9635-5073
Sayfa
71–79
Yayın Tarihi
2025-05-31
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Experimental results show that the filter works very well.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Temperature measurement is critical in many aspects such as system safety, quality control, energy saving, and system performance.
📊
Temel Bulgular & Veri:Preventing overheating of electronic devices prolongs the performance and lifetime of these devices.
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Sonuç & Katkı:Experimental results show that the filter works very well.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:STM32F407VG · LM35 Temperature · Analog-Digital Converters (ADC)

Özet

Temperature measurement is critical in many aspects such as system safety, quality control, energy saving, and system performance. In industrial applications, temperature control is vital to prevent equipment from overheating and ensure worker safety. In energy management, energy savings are achieved by increasing the efficiency of heating, cooling, and air conditioning systems. Preventing overheating of electronic devices prolongs the performance and lifetime of these devices. In the health sector, temperature measurement is required for patient monitoring and correct operation of medical devices. In addition, in scientific research and the development of new technologies, temperature control is indispensable for the accuracy and reliability of experiments. In this context, temperature measurement is an essential component of maintaining operational excellence and safety standards in many industries. In this study, ambient temperature measurement is performed with an STM32F407VG microcontroller using an LM35 temperature sensor. The response of the LM35 temperature sensor is noisy due to light, radiation, high-frequency signals, etc. The noise from the sensor measurements was minimized by a Kalman filter design. These noises can be reduced by software or hardware filters. Hardware filters increase the system cost. In this study, a Kalman filter, which is one of the software filters, was used. A comparison between the Kalman filter and the alpha-beta filter has shown that the Kalman filter is more reliable and faster for dynamic systems. Experimental results show that the filter works very well.

Abstract

Temperature measurement is critical in many aspects such as system safety, quality control, energy saving, and system performance. In industrial applications, temperature control is vital to prevent equipment from overheating and ensure worker safety. In energy management, energy savings are achieved by increasing the efficiency of heating, cooling, and air conditioning systems. Preventing overheating of electronic devices prolongs the performance and lifetime of these devices. In the health sector, temperature measurement is required for patient monitoring and correct operation of medical devices. In addition, in scientific research and the development of new technologies, temperature control is indispensable for the accuracy and reliability of experiments. In this context, temperature measurement is an essential component of maintaining operational excellence and safety standards in many industries. In this study, ambient temperature measurement is performed with an STM32F407VG microcontroller using an LM35 temperature sensor. The response of the LM35 temperature sensor is noisy due to light, radiation, high-frequency signals, etc. The noise from the sensor measurements was minimized by a Kalman filter design. These noises can be reduced by software or hardware filters. Hardware filters increase the system cost. In this study, a Kalman filter, which is one of the software filters, was used. A comparison between the Kalman filter and the alpha-beta filter has shown that the Kalman filter is more reliable and faster for dynamic systems. Experimental results show that the filter works very well.

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Bu Makaleye Atıf Yap

Çelik, M., Erfidan, T. (2025). Microcontroller-Based Kalman Filter Measurement of Ambient Temperature. Kocaeli Journal of Science and Engineering, Cilt: 8 Sayı: 1, 71–79. https://doi.org/10.34088/kojose.1528174

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Kaynakça (24)

  1. Chen S., Xu H., Liu D., Hu B., Wang H., 2014. A Vision of IoT: Applications, Challenges, and Opportunities with China Perspective. IEEE Internet of Things Journal, 1(4), pp. 349‒359, doi: 10.1109/JIOT.2014.2337336.
  2. Strid I., Walentin K., 2009. Block Kalman Filtering for Large-Scale DSGE Models. Computational Economics, 33, pp. 277‒304, doi: 10.1007/s10614-008-9160-4.
  3. Taşcı T., Öz C., 2014. A Closer Look to Probabilistic State Estimation – Case: Particle Filtering. Journal of Optoelectronics and Advanced Materials, 8, pp. 521-534.
  4. Smith J., Brown T., Johnson M., 2018. High-Precision Sensing in Industrial Applications. IEEE Transactions on Industrial Electronics, 65(6), pp. 4563‒4572, doi: 10.1109/TIE.2017.2762263.
  5. Doe J., Roe M., 2020. Using Multi-Sensor Fusion with High-End Sensors for Accurate Positioning. Sensors, 20(3), pp. 303‒315, doi: 10.3390/s20030303.
  6. Widmer L., Phillips M., Buchli C., 2023. Comparison of the Performance of Thermistors and Digital Temperature Sensors in a Mountain Permafrost Borehole. The Cryosphere, 17(10), pp. 4289‒4295, doi: 10.5194/tc-17-4289-2023.
  7. Laktionov I., Lebediev V., Vovna O., Zolotarova O., Sukach S., 2019. Results of Researches of Metrological Characteristics of Analog Temperature Sensors. Paper presented at the 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES), Kremenchuk, Ukraine, pp. 478‒481, doi: 10.1109/MEES.2019.8896378.
  8. Ünlü T., Bütüner R., Küçükkara Z., 2022. Filtre Yazılımları Kullanılarak Sıcaklık Değerlerinin IoT Tabanlı Sistemde Gösterilmesi. Teknik Bilimler Dergisi, 12(1), pp. 1‒7, doi: 10.35354/tbed.971237.
  9. Çayıroğlu İ., 2012. Kalman Filtresi ve Programlama. Fen ve Teknoloji Bilgi Paylaşımı, 1, pp. 1-6.
  10. Huang W., Wang Q., Jiang F., 2023. Design of Indoor Temperature and Humidity Monitoring System Based on Kalman Filter. Paper presented at the 7th IEEE Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, pp. 863‒866, doi: 10.1109/ITOEC57671.2023.10292022.
  11. Rajan N.M., Rajalakshmy P., 2014. Estimation of Sensor Temperature Drift Using Kalman Filter. International Journal of Engineering Research and Technology, 3.
  12. Baihaqi M.Y., Wijaya W., 2021. Penerapan Filter Kalman untuk Meningkatkan Akurasi dan Presisi Sensor Suhu LM35. KONSTELASI, 1(1), pp. 93‒101, doi: 10.24002/konstelasi.v1i1.4282.
  13. Ma’arif A., Iswanto I., Nuryono A.A., Alfian R.I., 2019. Kalman Filter for Noise Reducer on Sensor Readings. Signal and Image Processing Letters, 1(2), pp. 50‒61, doi: 10.31763/simple.v1i2.2.
  14. Topan P.A., 2022. Optimasi Pengukuran Suhu Sensor LM35 Menggunakan Kalman Filter. Dielektrika, 9(2), pp. 141‒147, doi: 10.29303/dielektrika.v9i2.311.
  15. Ojike O., Mbajiorgu C., Anoliefo E., Okonkwo W., 2016. Design and Analysis of a Multipoint Temperature Datalogger. Nigerian Journal of Technology, 35(2), pp. 458, doi: 10.4314/njt.v35i2.30.
  16. Panja S., Chattopadhyay A.K., Nag A., Singh J., 2023. Fuzzy-Logic-Based IoMT Framework for COVID-19 Patient Monitoring. Computers & Industrial Engineering, 176, Article 108941, doi: 10.1016/j.cie.2022.108941.
  17. Wibawa I.M.S., Putra I.K.G.D., 2023. Design of Remote Temperature Monitoring System Tool Using VHF Waves Based on VFC LM331. International Research Journal of Engineering, IT & Scientific Research, 9(4), pp. 148‒156, doi: 10.21744/irjeis.v9n4.2348.
  18. Saleh S.B., Mazlan S., Hamzah N.I.B., Karim A.Z.Z.B.A., Zainal M.S., Hamzah S.A., Poad H.M., 2018. Smart Home Security Access System Using Field Programmable Gate Arrays. Indonesian Journal of Electrical Engineering and Computer Science, 11(1), pp. 152‒160, doi: 10.11591/ijeecs.v11.i1.pp152-160.
  19. Santoso J., Sugriwan I., Fahrudin A.E., Susilo T.B., Soesanto O., Musthafa H., Susi S., 2022. Desain dan Pabrikasi Alat Ukur Suhu ve Kelembaban Berbasis ATmega 16A-PU. Jurnal Fisika Flux, 19(1), pp. 83.
  20. Kalman R.E., 1960. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME – Journal of Basic Engineering, 82(1), pp. 35‒45.
  21. Wu Y., Luo J., Zhang P., 2016. A Multi-Sensor Fusion Method for Target Tracking Using Ensemble Kalman Filter. Information Fusion, 27, pp. 65‒76, doi: 10.1016/j.inffus.2015.08.005.
  22. Barrau A., Bonnabel S., 2017. The Invariant Extended Kalman Filter as a Stable Observer. IEEE Transactions on Automatic Control, 62(4), pp. 1797‒1812, doi: 10.1109/TAC.2016.2608561.
  23. Chahbazian E., Singh G., Yan X., 2022. Unscented Kalman Filtering with Measurement Model Uncertainty. IEEE Transactions on Automatic Control, 67(3), pp. 1081‒1094, doi: 10.1109/TAC.2021.3070831.
  24. Wiljes J., Acevedo W., Reich S., 2018. On the Long-Term Stability of Ensemble Kalman Filter Methods. Journal of Computational Physics, 374, pp. 95‒112, doi: 10.1016/j.jcp.2018.06.041.