Noise reduction of acoustic Doppler velocimeter data based on Kalman filtering and autoregressive moving average models
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Noise reduction of acoustic Doppler velocimeter data based on Kalman filtering and autoregressive moving average models Chuanjiang Huang1, 2, 3, Fangli Qiao1, 2, 3*, Hongyu Ma1, 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
Received 27 February 2020; accepted 30 May 2020 © Chinese Society for Oceanography and Springer-Verlag GmbH Germany, part of Springer Nature 2020
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 midrange 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. Key words: noise, Kalman filtering, autoregressive moving average model, turbulence, acoustic Doppler velocimeter Citation: Huang Chuanjiang, Qiao Fangli, Ma Hongyu. 2020. Noise reduction of acoustic Doppler velocimeter data based on Kalman filtering and autoregressive moving average models. Acta Oceanologica Sinica, doi: 10.1007/s13131-020-1641-x
1 Introduction The acoustic Doppler velocimeter (ADV) can effectively measure three-dimensional instantaneous velocities at relatively high sampling rates. It has been widely used for estimates of turbulence characteristics in hydrologic and hydraulic measurements (Chang et al., 2019; Qi et al., 2020). However, turbulence measurements made by the ADV suffer from random spikes (Goring and Nikora, 2002) and noise (Voulgaris and Trowbridge, 1998; Nikora and Goring, 1998). Adequate post-processing, including de-spiking and de-noising, is necessary to obtain reliable turbulence parameters. The spikes in ADV datasets are mainly induced by aliasing of the Doppler signal (McLelland and Nicholas, 2000). A number of methods of de-spiking have been proposed (Goring and Nikora, 2002; Wahl, 2003; Mori et al., 2007; Parsheh et al., 2010; Islam and Zhu, 2014; Dilling and MacVicar, 2017), and can eliminate spurious outliers and spikes in ADV time series. There are three catego
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