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Temperature Sensor Denoising Algorithm Based on Curve Fitting and Compound Kalman Filtering
One of the most important ocean water parameters in world ocean observations is temperature. In the application of high-precision ocean sensors, there are often various interferences and random noises. These noises will cause the linearity of the sensor to change, and it is difficult to estimate the...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180457/ https://www.ncbi.nlm.nih.gov/pubmed/32244400 http://dx.doi.org/10.3390/s20071959 |
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author | Zhang, Yang Wang, Rong Li, Shouzhe Qi, Shengbo |
author_facet | Zhang, Yang Wang, Rong Li, Shouzhe Qi, Shengbo |
author_sort | Zhang, Yang |
collection | PubMed |
description | One of the most important ocean water parameters in world ocean observations is temperature. In the application of high-precision ocean sensors, there are often various interferences and random noises. These noises will cause the linearity of the sensor to change, and it is difficult to estimate the statistical characteristics, and the results will deviate from the real temperature. Aiming at the problems in the application, this paper proposes a compound Kalman smoothing filter (CKSF) algorithm based on least square curve fitting. This algorithm first analyzes the system model of the sensor, uses the least square method to fit the theoretical data and eliminate the non-linear factors caused by system itself, then estimates the statistical characteristics of the noise required by modeling, using the wavelet transform method to track the change of noise in real time and to accurately estimate the noise variance. Finally, a compound filtering method including wavelet transform and Kalman smoothing filtering is used as the main denoising algorithm, which is more accurate than a single Kalman filtering result. The algorithm is applied to the temperature measurement process of the ocean temperature sensor. The results show that the accuracy and stability of the sensor are improved. |
format | Online Article Text |
id | pubmed-7180457 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-71804572020-05-01 Temperature Sensor Denoising Algorithm Based on Curve Fitting and Compound Kalman Filtering Zhang, Yang Wang, Rong Li, Shouzhe Qi, Shengbo Sensors (Basel) Article One of the most important ocean water parameters in world ocean observations is temperature. In the application of high-precision ocean sensors, there are often various interferences and random noises. These noises will cause the linearity of the sensor to change, and it is difficult to estimate the statistical characteristics, and the results will deviate from the real temperature. Aiming at the problems in the application, this paper proposes a compound Kalman smoothing filter (CKSF) algorithm based on least square curve fitting. This algorithm first analyzes the system model of the sensor, uses the least square method to fit the theoretical data and eliminate the non-linear factors caused by system itself, then estimates the statistical characteristics of the noise required by modeling, using the wavelet transform method to track the change of noise in real time and to accurately estimate the noise variance. Finally, a compound filtering method including wavelet transform and Kalman smoothing filtering is used as the main denoising algorithm, which is more accurate than a single Kalman filtering result. The algorithm is applied to the temperature measurement process of the ocean temperature sensor. The results show that the accuracy and stability of the sensor are improved. MDPI 2020-03-31 /pmc/articles/PMC7180457/ /pubmed/32244400 http://dx.doi.org/10.3390/s20071959 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Yang Wang, Rong Li, Shouzhe Qi, Shengbo Temperature Sensor Denoising Algorithm Based on Curve Fitting and Compound Kalman Filtering |
title | Temperature Sensor Denoising Algorithm Based on Curve Fitting and Compound Kalman Filtering |
title_full | Temperature Sensor Denoising Algorithm Based on Curve Fitting and Compound Kalman Filtering |
title_fullStr | Temperature Sensor Denoising Algorithm Based on Curve Fitting and Compound Kalman Filtering |
title_full_unstemmed | Temperature Sensor Denoising Algorithm Based on Curve Fitting and Compound Kalman Filtering |
title_short | Temperature Sensor Denoising Algorithm Based on Curve Fitting and Compound Kalman Filtering |
title_sort | temperature sensor denoising algorithm based on curve fitting and compound kalman filtering |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180457/ https://www.ncbi.nlm.nih.gov/pubmed/32244400 http://dx.doi.org/10.3390/s20071959 |
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