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Thermal Conduction Kalman Filter for Enhanced Dynamic Response of Raman Distributed Temperature Sensing
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DOI：10.1109/tim.2026.3718109.png)
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Abstract
En 中文
The dynamic response of Raman distributed temperature sensing (RDTS) faces dual challenges of cumulative averaging delay and thermal hysteresis in armored optical cables. In this article, a thermal conduction Kalman filter (TC-KF) algorithm is proposed to address these issues. By embedding the heat conduction equation into a Kalman filter framework, the proposed algorithm enables denoising of high-frequency noisy data under a low cumulative averaging mode while compensating for the thermal hysteresis of armored optical cables. Experimental results demonstrate that TC-KF improves the signal-to-noise ratio (SNR) by 12.88 dB and requires only 6.42 ms for single-frame processing. Repeated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$30~^{\circ } $ </tex-math></inline-formula>C heating and cooling experiments further demonstrate that, under the same low cumulative averaging condition, TC-KF reduces the response time from 161.10 to 131.50 s in heating and from 520.30 to 500.22 s in cooling. The average expanded uncertainty is also lower than that of the benchmark methods. To the best of our knowledge, this work is the first to identify packaging-induced thermal hysteresis in armored optical cables as a practical dynamic-response issue in RDTS, and to provide an experimentally validated compensation method.
Keywords:
Augmented Kalman filter
optical fiber sensor
Raman distributed temperature sensing (RDTS)
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5.9
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1.9W
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5.8W
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