Noise reduction of acoustic Doppler velocimeter data based on Kalman filtering and autoregressive moving average models

Chuanjiang Huang; Fangli Qiao; Hongyu Ma

doi:10.1007/s13131-020-1641-x

Volume 39 Issue 12

Jan. 2021

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Chuanjiang Huang, Fangli Qiao, Hongyu Ma. Noise reduction of acoustic Doppler velocimeter data based on Kalman filtering and autoregressive moving average models[J]. Acta Oceanologica Sinica, 2020, 39(12): 106-113. doi: 10.1007/s13131-020-1641-x

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Chuanjiang Huang, Fangli Qiao, Hongyu Ma. Noise reduction of acoustic Doppler velocimeter data based on Kalman filtering and autoregressive moving average models[J]. Acta Oceanologica Sinica, 2020, 39(12): 106-113. doi: 10.1007/s13131-020-1641-x

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Noise reduction of acoustic Doppler velocimeter data based on Kalman filtering and autoregressive moving average models

doi: 10.1007/s13131-020-1641-x

Chuanjiang Huang^{1, 2, 3},
Fangli Qiao^{1, 2, 3
,
,},
Hongyu Ma^{1, 3}

1.
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
2.
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
3.
Key Laboratory of Marine Sciences and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China

Funds: The National Key Research and Development Program of China under contract No. 2017YFC1404000; the Basic Scientific Fund for National Public Research Institutes of China under contract No. 2018S03; the National Natural Science Foundation of China under contract Nos 41776038 and 41821004.

More Information

Corresponding author: Email: qiaofl@fio.org.cn
Received Date: 2020-02-27
Accepted Date: 2020-05-30

Available Online: 2021-04-21

Publish Date: 2020-12-25

Abstract

Abstract

Oceanic turbulence measurements made by an acoustic Doppler velocimeter (ADV) suffer from noise that potentially affects the estimates of turbulence statistics. This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations. Results show that the two methods have similar performance in ADV de-noising, and both effectively reduce noise in ADV velocities, even in cases of high noise. They eliminate the noise floor at high frequencies of the velocity spectra, leading to a longer range that effectively fits the Kolmogorov −5/3 slope at mid-range frequencies. After de-noising adopting the two methods, the values of the mean velocity are almost unchanged, while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments. The Reynolds stress is also affected by high noise levels, and de-noising thus reduces uncertainties in estimating the Reynolds stress.
- noise,
- Kalman filtering,
- autoregressive moving average model,
- turbulence,
- acoustic Doppler velocimeter

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References(34)

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