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Journal articles on the topic 'Adaptive filtering'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Wagner, Christian, and Gabriel Wittum. "Adaptive filtering." Numerische Mathematik 78, no. 2 (1997): 305–28. http://dx.doi.org/10.1007/s002110050314.

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2

Brown, S. D., and S. C. Rutan. "Adaptive Kalman Filtering." Journal of Research of the National Bureau of Standards 90, no. 6 (1985): 403. http://dx.doi.org/10.6028/jres.090.032.

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3

Troncoso-Pastoriza, Juan Ramón, and Fernando Perez-Gonzalez. "Secure Adaptive Filtering." IEEE Transactions on Information Forensics and Security 6, no. 2 (2011): 469–85. http://dx.doi.org/10.1109/tifs.2011.2109385.

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4

Shynk, J. J. "Adaptive IIR filtering." IEEE ASSP Magazine 6, no. 2 (1989): 4–21. http://dx.doi.org/10.1109/53.29644.

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5

RAUF, FAWAD, and HASSAN M. AHMED. "NONLINEAR ADAPTIVE FILTERING." International Journal of High Speed Electronics and Systems 07, no. 04 (1996): 491–520. http://dx.doi.org/10.1142/s0129156496000281.

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6

Singher, Liviu. "Adaptive multiple filtering." Optical Engineering 41, no. 1 (2002): 55. http://dx.doi.org/10.1117/1.1425790.

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7

Yukawa, Masahiro. "Multikernel Adaptive Filtering." IEEE Transactions on Signal Processing 60, no. 9 (2012): 4672–82. http://dx.doi.org/10.1109/tsp.2012.2200889.

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8

Rutan, Sarah C. "Adaptive Kalman Filtering." Analytical Chemistry 63, no. 22 (1991): 1103A—1109A. http://dx.doi.org/10.1021/ac00022a739.

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9

Bose, Tamal, Anand Venkatachalam, and Ratchaneekorn Thamvichai. "Multiplierless Adaptive Filtering." Digital Signal Processing 12, no. 1 (2002): 107–18. http://dx.doi.org/10.1006/dspr.2001.0407.

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10

Wang, Hong, Hongyu Han, Sheng Zhang, and Jinhua Ku. "An efficient kernel adaptive filtering algorithm with adaptive alternating filtering mechanism." Digital Signal Processing 159 (April 2025): 104997. https://doi.org/10.1016/j.dsp.2025.104997.

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11

Voskoboinikov, Yuri E. "А locally adaptive wavelet filtering algorithm for images". Analysis and data processing systems, № 1 (29 березня 2023): 25–36. http://dx.doi.org/10.17212/2782-2001-2023-1-25-36.

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The algorithms based on the decomposition of a noisy image in an orthogonal basis of wavelet functions have been widely used to filter images (especially contrasting ones) over the past four decades. In this case, most wavelet filtering algorithms are of a threshold nature, namely: the decomposition coefficient smaller in an absolute value of a certain threshold value is reset to zero; otherwise the coefficient undergoes some (most often nonlinear) transformation. A certain (and very significant) drawback of threshold algorithms is that all coefficients of a certain decomposition level are pro

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12

Xianqiang, Cui, and Yang Yuanxi. "Adaptively robust filtering with classified adaptive factors." Progress in Natural Science 16, no. 8 (2006): 846–51. http://dx.doi.org/10.1080/10020070612330078.

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13

Linovich, A. Yu, V. S. Litvinova, and M. D. Korolev. "COMB ADAPTIVE FILTERING ALGORITHM." Vestnik of Ryazan State Radio Engineering University 77 (2021): 3–16. http://dx.doi.org/10.21667/1995-4565-2021-77-3-16.

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The problem of multipath channel frequency response equalization in a receiver is considered. The aim is to develop an algorithm of comb adaptive filtering, which makes possible, on the one hand, to provide high rate of multirate receiver system adaptation, and on the other hand, to reduce computational complexities of real-time processing. The robustness analysis of the suggested algorithm is carried out. Two variants of comb adaptive filter are studied. For the second one a fast modification is proposed. On the assumption of multichannel communication system equalizer realization the develop

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14

WEI, Bao-guo. "Improved adaptive median filtering." Journal of Computer Applications 28, no. 7 (2008): 1732–34. http://dx.doi.org/10.3724/sp.j.1087.2008.01732.

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15

Gavaskar, Ruturaj G., and Kunal N. Chaudhury. "Fast Adaptive Bilateral Filtering." IEEE Transactions on Image Processing 28, no. 2 (2019): 779–90. http://dx.doi.org/10.1109/tip.2018.2871597.

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16

Kasparis, T., and J. Lane. "Adaptive scratch noise filtering." IEEE Transactions on Consumer Electronics 39, no. 4 (1993): 917–22. http://dx.doi.org/10.1109/30.267417.

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17

Yashin, A. "Continuous-time adaptive filtering." IEEE Transactions on Automatic Control 31, no. 8 (1986): 776–79. http://dx.doi.org/10.1109/tac.1986.1104380.

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18

Goldstein, J. S., and I. S. Reed. "Reduced-rank adaptive filtering." IEEE Transactions on Signal Processing 45, no. 2 (1997): 492–96. http://dx.doi.org/10.1109/78.554317.

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19

Garber, Dan, and Elad Hazan. "Adaptive Universal Linear Filtering." IEEE Transactions on Signal Processing 61, no. 7 (2013): 1595–604. http://dx.doi.org/10.1109/tsp.2012.2234742.

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20

Thyssen, Jes, and Juin-Hwey Chen. "Method for adaptive filtering." Journal of the Acoustical Society of America 125, no. 6 (2009): 4108. http://dx.doi.org/10.1121/1.3155492.

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21

Lukac, Rastislav. "Adaptive vector median filtering." Pattern Recognition Letters 24, no. 12 (2003): 1889–99. http://dx.doi.org/10.1016/s0167-8655(03)00016-3.

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22

Scarpiniti, Michele, Danilo Comminiello, Raffaele Parisi, and Aurelio Uncini. "Nonlinear spline adaptive filtering." Signal Processing 93, no. 4 (2013): 772–83. http://dx.doi.org/10.1016/j.sigpro.2012.09.021.

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23

Akarun, Lale, and Richard A. Haddad. "Adaptive decimated median filtering." Pattern Recognition Letters 13, no. 1 (1992): 57–62. http://dx.doi.org/10.1016/0167-8655(92)90114-f.

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24

Lukac, Rastislav, Viktor Fischer, Guy Motyl, and Milos Drutarovsky. "Adaptive video filtering framework." International Journal of Imaging Systems and Technology 14, no. 6 (2004): 223–37. http://dx.doi.org/10.1002/ima.20027.

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25

Yamini Swathi, L., K. S S Soujanya, N. P Lavanya Kumari, and M. Purnachandra Rao. "Study of LMS Algorithm Using Adaptive Filtering Technique." International Journal of Scientific Engineering and Research 3, no. 10 (2015): 66–70. https://doi.org/10.70729/ijser15459.

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26

Xue, Li, Shesheng Gao, and Yongmin Zhong. "Robust Adaptive Unscented Particle Filter." International Journal of Intelligent Mechatronics and Robotics 3, no. 2 (2013): 55–66. http://dx.doi.org/10.4018/ijimr.2013040104.

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This paper presents a new robust adaptive unscented particle filtering algorithm by adopting the concept of robust adaptive filtering to the unscented particle filter. In order to prevent particles from degeneracy, this algorithm adaptively determines the equivalent weight function according to robust estimation and adaptively adjusts the adaptive factor constructed from predicted residuals to resist the disturbances of singular observations and the kinematic model noise. It also uses the unscented transformation to improve the accuracy of particle filtering, thus providing the reliable state

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27

Zhang, Yun, Dean Zhao, Jun Zhang, and Yun Liu. "Research on Anti-Windup Controller of Autonomous Navigation Vehicle Based on Improved Adaptive Filter." Advanced Materials Research 1025-1026 (September 2014): 1119–24. http://dx.doi.org/10.4028/www.scientific.net/amr.1025-1026.1119.

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This paper presents relevant methods on navigation accuracy improvement of agricultural vehicle focusing on positioning accuracy and control precision. An adaptive kalman filtering, combination of Sage_Husa adaptive filtering and strong tracking kalman filtering based on strict convergence criterion, is adopted to improve filtering accuracy with strong ability of adaptive filtering and restraining filter divergence. A new variable-structure switching method to prevent PID controller from integrator windup can effectively solve the integral saturation phenomenon, which adopts a kind of adaptive

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28

Liu, Ji Guang, and Hai Yang Wang. "The Realization of Fuzzy Adaptive Filtering Algorithm." Applied Mechanics and Materials 182-183 (June 2012): 1733–37. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1733.

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This paper introduces a kind of fuzzy adaptive filtering algorithm. The whole process is divided into four steps. Plenty experimental simulation have been made, which has a good results using these methods. On this the premise which the signal detail is not damaged, this filtering algorithm can not only remove pulse but also has a higher capability of noise reduction. It have been verified by actual use and experimental simulation that this filtering algorithm not only has the all advantages of mean filtering and median filtering but can avoid edge blurry of signal, which can’t be realized usi

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29

Lu, Yang, and Xiaoli Wang. "EFFECT OF METABOLIC ENZYMES ON ACCELERATION ABILITY IN EXERCISE FATIGUE." Revista Brasileira de Medicina do Esporte 27, no. 3 (2021): 257–61. http://dx.doi.org/10.1590/1517-8692202127032021_0128.

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ABSTRACT Introduction Study the relationship between the metabolic enzyme and the biological image, filtered by an adaptive filtering algorithm. Objective The research aims to In this study, human metabolic enzymes were evaluated by electrocardiogram and electromyogram images, and an adaptive filtering algorithm removed the noises in the images. Methods The electrocardiogram and electromyogram images at different periods were obtained, and the calculation method and application scope of the adaptive filtering algorithm were analysed. Results Adaptive filter was designed by the combination of a

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30

Bo, Lei, and Xin Yan Zhu. "A Simaplied Adaptive Kalman Filtering Algorithm." Advanced Materials Research 1037 (October 2014): 378–82. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.378.

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The adaptive Kalman filtering algorithm was adopted in the online estimate of navigation state of unmanned aerial vehicle (UAV) as the simplified model often used. At the moment, the alogorithms those usually applied in this territory are not perfect. Analysed the adaptive Kalman filtering based on Maximum-Likelihood Estimation and Sage-Husa Kalman filtering, take advantage the characteristics of residue, choose the estimation windows, a simplified adaptive Kalman filtering algorithm was gived.

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31

Schnaufer, B. A., and W. K. Jenkins. "Adaptive fault tolerance for reliable LMS adaptive filtering." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 44, no. 12 (1997): 1001–14. http://dx.doi.org/10.1109/82.644560.

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32

Widrow, Bernard, Gregory Plet, Edson Ferreira, and Marcelo Lamego. "Adaptive Inverse Control Based on Nonlinear Adaptive Filtering." IFAC Proceedings Volumes 31, no. 4 (1998): 211–16. http://dx.doi.org/10.1016/s1474-6670(17)42160-4.

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33

Dogariu, Laura-Maria, Cristian-Lucian Stanciu, Camelia Elisei-Iliescu, Constantin Paleologu, Jacob Benesty, and Silviu Ciochină. "Tensor-Based Adaptive Filtering Algorithms." Symmetry 13, no. 3 (2021): 481. http://dx.doi.org/10.3390/sym13030481.

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Tensor-based signal processing methods are usually employed when dealing with multidimensional data and/or systems with a large parameter space. In this paper, we present a family of tensor-based adaptive filtering algorithms, which are suitable for high-dimension system identification problems. The basic idea is to exploit a decomposition-based approach, such that the global impulse response of the system can be estimated using a combination of shorter adaptive filters. The algorithms are mainly tailored for multiple-input/single-output system identification problems, where the input data and

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34

Musikhin, Vladislav I. "Polyspectral Processing in Adaptive Filtering." Ural Radio Engineering Journal 2, no. 3 (2018): 32–41. http://dx.doi.org/10.15826/urej.2018.2.3.003.

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35

Benzemrane, Khadidja, Gilney Damm, and Giovanni L. Santosuosso. "Adaptive Observer and Kalman Filtering." IFAC Proceedings Volumes 41, no. 2 (2008): 3865–70. http://dx.doi.org/10.3182/20080706-5-kr-1001.00650.

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36

Sadik, A. Z., Z. M. Hussain, and P. O'Shea. "Adaptive algorithm for ternary filtering." Electronics Letters 42, no. 7 (2006): 420. http://dx.doi.org/10.1049/el:20064257.

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37

Zou, Y., S. C. Chan, and T. S. Ng. "Robust M-estimate adaptive filtering." IEE Proceedings - Vision, Image, and Signal Processing 148, no. 4 (2001): 289. http://dx.doi.org/10.1049/ip-vis:20010316.

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38

Usevitch, B. E., and M. T. Orchard. "Adaptive filtering using filter banks." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 43, no. 3 (1996): 255–65. http://dx.doi.org/10.1109/82.486471.

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39

Seong Rag Kim and A. Efron. "Adaptive robust impulse noise filtering." IEEE Transactions on Signal Processing 43, no. 8 (1995): 1855–66. http://dx.doi.org/10.1109/78.403344.

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40

Clarkson, P. M., P. R. White, and J. A. Mardell. "Adaptive filtering for speech enhancement." Journal of the Acoustical Society of America 80, S1 (1986): S20. http://dx.doi.org/10.1121/1.2023697.

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41

Bai Xiaodong, 白晓东, 舒勤 Shu Qin, 杜小燕 Du Xiaoyan, and 黄燕琴 Huang Yanqin. "Improved Adaptive Bilateral Filtering Algorithm." Laser & Optoelectronics Progress 57, no. 4 (2020): 041003. http://dx.doi.org/10.3788/lop57.041003.

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42

Robertson, Stephen, and Stephen Walker. "Threshold setting in adaptive filtering." Journal of Documentation 56, no. 3 (2000): 312–31. http://dx.doi.org/10.1108/eum0000000007118.

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43

Jojoa, Pablo E., Max Gerken, and Felipe M. Pait. "The Accelerating Adaptive Filtering Algorithm." IFAC Proceedings Volumes 34, no. 14 (2001): 331–35. http://dx.doi.org/10.1016/s1474-6670(17)41643-0.

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44

Sridharan, M. K. "Subband adaptive filtering: Oversampling approach." Signal Processing 71, no. 1 (1998): 101–4. http://dx.doi.org/10.1016/s0165-1684(98)00173-x.

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45

Peng, Siyuan, Badong Chen, Lei Sun, Wee Ser, and Zhiping Lin. "Constrained maximum correntropy adaptive filtering." Signal Processing 140 (November 2017): 116–26. http://dx.doi.org/10.1016/j.sigpro.2017.05.009.

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46

Diniz, Paulo S. R. "On Data-Selective Adaptive Filtering." IEEE Transactions on Signal Processing 66, no. 16 (2018): 4239–52. http://dx.doi.org/10.1109/tsp.2018.2847657.

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47

Durak, L., and S. Aldirmaz. "Adaptive fractional Fourier domain filtering." Signal Processing 90, no. 4 (2010): 1188–96. http://dx.doi.org/10.1016/j.sigpro.2009.10.002.

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48

Lee, Y., M. Oh, and V. I. Shin. "Adaptive nonlinear continuous-discrete filtering." Applied Numerical Mathematics 47, no. 1 (2003): 45–56. http://dx.doi.org/10.1016/s0168-9274(03)00103-x.

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49

Ujang, Bukhari Che, Clive Cheong Took, and Danilo P. Mandic. "Split quaternion nonlinear adaptive filtering." Neural Networks 23, no. 3 (2010): 426–34. http://dx.doi.org/10.1016/j.neunet.2009.10.006.

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50

Ioannou, P. "Adaptive filtering, prediction and control." Automatica 21, no. 5 (1985): 616–18. http://dx.doi.org/10.1016/0005-1098(85)90014-7.

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