# Applications of adaptive variable step-size algorithm in turbulence observation system

Volume 16, Issue 2, pp 218-226
Publication Date: June 15, 2016 Submission Date: March 02, 2016
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### Authors

Yongfang Wang - School of Informatics, Linyi University, Linyi, Shandong 276005, P. R. China. Chengdong Yang - School of Informatics, Linyi University, Linyi, Shandong 276005, P. R. China. Jianlong Qiu - School of Science, Linyi University, Linyi, Shandong 276005, P. R. China.

### Abstract

In turbulence observation system, noise signal is random and difficult to identify, which will pollute the real signal and affect the quality of the data. To eliminate the noise signal, the article puts forward a kind of adaptive variable step-size de-noising algorithm. Firstly, raw data is changed into corresponding physical parameters, and spectral analysis is used to analyze the relationship among these parameters, and then, according to the correlation to construct the variable step-size de-noising algorithm, and through error to adjust shape of the step size factor to control the optimal weight coefficient. Finally, simulation and observation data is used to verify the effectiveness of the algorithm, and Goodman's filter algorithm is compared with the algorithm. The results show that the algorithm has higher precision and the noise is effectively reduced.

### Share and Cite

##### ISRP Style

Yongfang Wang, Chengdong Yang, Jianlong Qiu, Applications of adaptive variable step-size algorithm in turbulence observation system, Journal of Mathematics and Computer Science, 16 (2016), no. 2, 218-226

##### AMA Style

Wang Yongfang, Yang Chengdong, Qiu Jianlong, Applications of adaptive variable step-size algorithm in turbulence observation system. J Math Comput SCI-JM. (2016); 16(2):218-226

##### Chicago/Turabian Style

Wang, Yongfang, Yang, Chengdong, Qiu, Jianlong. "Applications of adaptive variable step-size algorithm in turbulence observation system." Journal of Mathematics and Computer Science, 16, no. 2 (2016): 218-226

### Keywords

• Variable step-size
• spectral analysis
• turbulence observation system.

•  68Q25
•  68Q32
•  68W40

### References

• [1] E. S. Bakunova, J. Harlim, Optimal filtering of complex turbulent systems with memory depth through consistency constraints, J. Comput. Phys., 237 (2013), 320-343.

• [2] G. A. Einicke, G. Falco, J. T. Malos, EM Algorithm State Matrix Estimation for Navigation, IEEE Signal Processing Letters, 17 (2010), 437-440.

• [3] L. Goodman, E. R. Levine, R. G. Lueck, On Measuring the terms of the turbulent kinetic energy budget from an AUV, J. Atmospheric Oceanic Tech., 23 (2006), 977-990.

• [4] B. Hassibi, A. H. Sayed, T. Kailath, $H^{\infty}$ optimality of the LMS algorithm, IEEE Transactions on Signal Processing, 44 (1996), 267-280.

• [5] W. A. Kareem, T. Nabil, S. Izawa, Y. Fukunishi, Harmonic analysis filtering techniques for forced and decaying homogeneous isotropic turbulence, Comput. Math. Appl., 65 (2013), 1059-1085.

• [6] J. L. Kitchen, J. D. Moore, S. A. Palmer, R. G. Allaby, MCMC-ODPR: Primer design optimization using Markov Chain Monte Carlo sampling , BMC Bioinformatics , 2012 (2012 ), 10 pages.

• [7] S. Lan, Y. Liu, Y. Wang, J. Liu, Z. Wang, Vibration source analysis and vibration reduction for a vertical microstructure turbulence profiler, J. Vib. Shock, 31 (2012), 5-9.

• [8] T. D. Mudge, R. G. Lueck, Digital signal processing to enhance oceanographic observations, J. Atmospheric Oceanic Tech., 11 (1994), 825-836.

• [9] T. Prestero, Verification of a six-degree of freedom simulation model for the REMUS autonomous underwater vehicle, University of California, Davis (2001)

• [10] A. Soloviev, R. Lukas, P. Hacker, A near-surface microstructure sensor system used during TOGA COARE. Part II: turbulence measurements, J. Atmospheric Oceanic Tech., 16 (1999), 1598-1618.

• [11] D. Song, J. Sun, B. Xue, C. Tian, The fixed-point ocean turbulence observation on submerged buoy, IEEE International Conference on Intelligent Control, Automatic Detection and High-End Equipment (ICADE), (2012), 72-77.

• [12] S. Wang, X. Xiao, Y. Wang, Z. Wang, B. Chen, Denoising method for shear probe signal based on wavelet thresholding , Transactions of Tianjin University, 18 (2012), 135-140.

• [13] T. L. Wilfong, E. M. Lau, B. L. Weber, D. A. Merritt, S. A. McLaughlin, Median filter effects on radar wind profiler spectral noise statistics, J. Atmospheric Oceanic Tech., 31 (2014), 2088-2093.

• [14] J. Xu, K. Tang, X. Zhang , Study on mechanism and reduction of hydro-acoustical noise induced by flow over an open cavity based on numerical simulation , Chinese J. Hydrodyn., 5 (2014), 618-629

• [15] X. Zhong, A. B. Premkumar, W. Wang, Direction of arrival tracking of an underwater acoustic source using particle filtering: Real data experiments, TENCON Spring Conference, IEEE, (2013), 420-424.