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

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > Acta Oceanologica Sinica > 2020 > 39(12): 106-113

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

Citation:

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

Citation:

PDF( 1566 KB)

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

FullText(HTML)

References(34)

References

[1]	Bian Changwei, Liu Zhiyu, Huang Yongxiang, et al. 2018. On estimating turbulent Reynolds stress in wavy aquatic environment. Journal of Geophysical Research: Oceans, 123(4): 3060–3071. doi: 10.1002/2017JC013230
[2]	Bluteau C E, Jones N L, Ivey G N. 2011. Estimating turbulent kinetic energy dissipation using the inertial subrange method in environmental flows. Limnology and Oceanography: Methods, 9(7): 302–321. doi: 10.4319/lom.2011.9.302
[3]	Chang Yang, Chen Yining, Li Yan. 2019. Flow modification associated with mangrove trees in a macro-tidal flat, southern China. Acta Oceanologica Sinica, 38(2): 1–10. doi: 10.1007/s13131-018-1163-y
[4]	Chanson H, Trevethan M, Koch C. 2007. Discussion of “Turbulence measurements with acoustic Doppler velocimeters” by Carlos M. Garcìa, Mariano I. Cantero, Yarko Niño, and Marcelo H. Garcìa. Journal of Hydraulic Engineering, 133(11): 1283–1286
[5]	Dilling S, MacVicar B J. 2017. Cleaning high-frequency velocity profile data with autoregressive moving average (ARMA) models. Flow Measurement and Instrumentation, 54: 68–81. doi: 10.1016/j.flowmeasinst.2016.12.005
[6]	Durgesh V, Thomson J, Richmond M C, et al. 2014. Noise correction of turbulent spectra obtained from acoustic doppler velocimeters. Flow Measurement and Instrumentation, 37: 29–41. doi: 10.1016/j.flowmeasinst.2014.03.001
[7]	Elgar S, Raubenheimer B, Guza R T. 2005. Quality control of acoustic Doppler velocimeter data in the surfzone. Measurement Science Technology, 16(10): 1889–1893. doi: 10.1088/0957-0233/16/10/002
[8]	Fabozzi F J, Focardi S M, Rachev S T, et al. 2014. The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications. New Jersey: John Wiley & Sons, Inc, 171–190
[9]	Feddersen F. 2010. Quality controlling surf zone acoustic Doppler velocimeter observations to estimate the turbulent dissipation rate. Journal of Atmospheric and Oceanic Technology, 27(12): 2039–2055. doi: 10.1175/2010JTECHO783.1
[10]	García C M, Cantero M I, Niño Y, et al. 2005. Turbulence measurements with acoustic Doppler velocimeters. Journal of Hydraulic Engineering, 131(12): 1062–1073. doi: 10.1061/(ASCE)0733-9429(2005)131:12(1062)
[11]	Goring D G, Nikora V I. 2002. Despiking acoustic Doppler velocimeter data. Journal of Hydraulic Engineering, 128(1): 117–126. doi: 10.1061/(ASCE)0733-9429(2002)128:1(117)
[12]	Huang Chuanjiang, Ma Hongyu, Guo Jingsong, et al. 2018. Calculation of turbulent dissipation rate with acoustic Doppler velocimeter. Limnology and Oceanography: Methods, 16(5): 265–272. doi: 10.1002/lom3.10243
[13]	Hurther D, Lemmin U. 2001. A correction method for turbulence measurements with a 3D acoustic Doppler velocity profiler. Journal of Atmospheric and Oceanic Technology, 18(3): 446–458. doi: 10.1175/1520-0426(2001)018<0446:ACMFTM>2.0.CO;2
[14]	Islam M R, Zhu D Z. 2013. Kernel density-based algorithm for despiking ADV data. Journal of Hydraulic Engineering, 139(7): 785–793. doi: 10.1061/(ASCE)HY.1943-7900.0000734
[15]	Kalman R E. 1960. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1): 35–45. doi: 10.1115/1.3662552
[16]	Khorsandi B, Mydlarski L, Gaskin S. 2012. Noise in turbulence measurements using acoustic Doppler velocimetry. Journal of Hydraulic Engineering, 138(10): 829–838. doi: 10.1061/(ASCE)HY.1943-7900.0000589
[17]	Kirchner J W. 2005. Aliasing in 1/f ^α noise spectra: Origins, consequences, and remedies. Physical Review E, 71(6): 066110. doi: 10.1103/PhysRevE.71.066110
[18]	Lemmin U, Lhermitte R. 1999. ADV measurements of turbulence: Can we improve their interpretation?. Journal of Hydraulic Engineering, 125(9): 987–988. doi: 10.1061/(ASCE)0733-9429(1999)125:9(987)
[19]	McLelland S J, Nicholas A P. 2000. A new method for evaluating errors in high-frequency ADV measurements. Hydrological Processes, 14(2): 351–366. doi: 10.1002/(SICI)1099-1085(20000215)14:2<351::AID-HYP963>3.0.CO;2-K
[20]	Moore B. 2012. Kalman Filter Package, Version 1.0.0.0. Implements Kalman filter, extended Kalman filter, dual Kalman filter, and square root Kalman filters. www.mathworks.com/matlabcentral/fileexchange/38302-kalman-filter-package [2012-09-24/2019-09-03]
[21]	Mori N, Suzuki T, Kakuno S. 2007. Noise of acoustic Doppler velocimeter data in bubbly flows. Journal of Engineering Mechanics, 133(1): 122–125. doi: 10.1061/(ASCE)0733-9399(2007)133:1(122)
[22]	Neusser K. 2016. Time Series Econometrics. Switzerland: Springer International Publishing, 25–44
[23]	Nikora V I, Goring D G. 1998. ADV measurements of turbulence: Can we improve their interpretation?. Journal of Hydraulic Engineering, 124(6): 630–634. doi: 10.1061/(ASCE)0733-9429(1998)124:6(630)
[24]	Parsheh M, Sotiropoulos F, Porte-Agel F. 2010. Estimation of power spectra of acoustic-Doppler velocimetry data contaminated with intermittent spikes. Journal of Hydraulic Engineering, 136(6): 368–378. doi: 10.1061/(ASCE)HY.1943-7900.0000202
[25]	Pope S B. 2000. Turbulent Flows. Cambridge, UK: Cambridge University Press, 219–242
[26]	Qi Yongfeng, Shang Xiaodong, Chen Guiying, et al. 2020. Eddy covariance measurements of turbulent fluxes in the surf zone. Acta Oceanologica Sinica, 39(3): 63–72. doi: 10.1007/s13131-020-1562-8
[27]	Qiao Fangli, Yuan Yeli, Deng Jia, et al. 2016. Wave-turbulence interaction-induced vertical mixing and its effects in ocean and climate models. Philosophical Transactions of The Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065): 20150201. doi: 10.1098/rsta.2015.0201
[28]	Saddoughi S G, Veeravalli S V. 1994. Local isotropy in turbulent boundary layers at high Reynolds number. Journal of Fluid Mechanics, 268: 333–372. doi: 10.1017/S0022112094001370
[29]	SonTek. 2001. Acoustic Doppler velocimeter principles of operation. SonTek/YSI Technical Notes. San Diego: SonTek, 1–14
[30]	Variano E A, Cowen E A. 2008. A random-jet-stirred turbulence tank. Journal of Fluid Mechanics, 604: 1–32. doi: 10.1017/S0022112008000645
[31]	Voulgaris G, Trowbridge J H. 1998. Evaluation of the acoustic Doppler velocimeter (ADV) for turbulence measurements. Journal of Atmospheric and Oceanic Technology, 15(1): 272–289. doi: 10.1175/1520-0426(1998)015<0272:EOTADV>2.0.CO;2
[32]	Wahl T L. 2003. Discussion of “Despiking acoustic Doppler velocimeter data” by Derek G. Goring and Vladimir I. Nikora. Journal of Hydraulic Engineering, 129(6): 484–487
[33]	Welch G, Bishop G. 1995. An introduction to the Kalman filter. Technical report. Chapel Hill, NC, United States: University of North Carolina at Chapel Hill, 1–16
[34]	Wolk F, Yamazaki H, Seuront L, et al. 2002. A new free-fall profiler for measuring biophysical microstructure. Journal of Atmospheric and Oceanic Technology, 19(5): 780–793. doi: 10.1175/1520-0426(2002)019<0780:ANFFPF>2.0.CO;2

Relative Articles

Relative Articles

Supplements(0)

Cited By

Cited by

Periodical cited type(9)

1.	Mingyu Guo, Xiaobo Meng, Xinxing Chen, et al. Seismic Noise Suppression Method Based on Wave-Unet and Attention Mechanism. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 1. doi:10.1109/TGRS.2024.3438572
2.	Linwang Ding, Chenglin Wen. High-Order Extended Kalman Filter for State Estimation of Nonlinear Systems. Symmetry, 2024, 16(5): 617. doi:10.3390/sym16050617
3.	Farzad Asgari, Seyed Hossein Mohajeri, Mojtaba Mehraein. ProADV: A toolkit for enhancing water dynamics research using acoustic doppler velocimeter devices. SoftwareX, 2024, 27: 101868. doi:10.1016/j.softx.2024.101868
4.	Yue Chen, Wenjun Yang, Haili Lin. Reproducing the temporal averaging process of the acoustic Doppler velocimeter and assessing its impact on turbulence measurements. Measurement, 2024, 226: 114093. doi:10.1016/j.measurement.2023.114093
5.	Chuan Jiang Huang, Sheng Chen, Siyu Chen, et al. Observation and parameterization of turbulent Reynolds stress in the ocean surface boundary layer under swell-dominated conditions. Ocean Modelling, 2023, 185: 102239. doi:10.1016/j.ocemod.2023.102239
6.	Wenyi Zhang, Fei Teng, Jingyu Li, et al. Denoising method based on CNN-LSTM and CEEMD for LDV signals from accelerometer shock testing. Measurement, 2023, 216: 112951. doi:10.1016/j.measurement.2023.112951
7.	E. Fernandez-Rodriguez. On the impact of waves and turbulence on the power fluctuations and wake structure of a tidal-stream turbine. Physics of Fluids, 2023, 35(5) doi:10.1063/5.0142872
8.	Farzin Homayounfar, Babak Khorsandi. Evaluating acoustic Doppler velocimetry pulse-pair spacing/velocity range setting for turbulent flow measurements. Physics of Fluids, 2022, 34(3) doi:10.1063/5.0086303
9.	Chuan Jiang Huang, Fangli Qiao. Simultaneous Observations of Turbulent Reynolds Stress in the Ocean Surface Boundary Layer and Wind Stress over the Sea Surface. Journal of Geophysical Research: Oceans, 2021, 126(2) doi:10.1029/2020JC016839