Journal articles: 'Adaptive filtering'

1

Wagner, Christian, and Gabriel Wittum. "Adaptive filtering." Numerische Mathematik 78, no. 2 (December 1, 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 (November 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 (June 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 (April 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 (December 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 (January 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 (September 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 (November 15, 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 (January 2002): 107–18. http://dx.doi.org/10.1006/dspr.2001.0407.

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10

Voskoboinikov, Yuri E. "А locally adaptive wavelet filtering algorithm for images." Analysis and data processing systems, no. 1 (March 29, 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 processed with one identical threshold value (i.e., a constant value for all de-composition coefficients). This does not allow taking into account the “individual energy” of each decomposition coefficient for its more optimal processing. Therefore, we propose its own filtering factor for each coefficient, built on the basis of the optimal Wiener filtering and where a filtering parameter is introduced to compensate for incomplete a priori information on the value of the processed decomposition coefficients. In order to select a filtering parameter, a statistical approach has been proposed that makes it possible to estimate the optimal value of this parameter with acceptable accuracy. The performed computational experiment has shown the developed algorithm effectiveness for wavelet filtering of images.

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11

Xianqiang, Cui, and Yang Yuanxi. "Adaptively robust filtering with classified adaptive factors." Progress in Natural Science 16, no. 8 (August 1, 2006): 846–51. http://dx.doi.org/10.1080/10020070612330078.

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12

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 developed algorithm is able to provide the advantage in rate almost by a factor of ten and at the same time to reduce computational complexities by a factor of three as compared with the least-mean-square algorithm as the experimental results demonstrate.

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13

WEI, Bao-guo. "Improved adaptive median filtering." Journal of Computer Applications 28, no. 7 (November 3, 2008): 1732–34. http://dx.doi.org/10.3724/sp.j.1087.2008.01732.

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14

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

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15

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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16

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

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17

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

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18

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

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19

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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20

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

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21

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

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22

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

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23

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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24

Xue, Li, Shesheng Gao, and Yongmin Zhong. "Robust Adaptive Unscented Particle Filter." International Journal of Intelligent Mechatronics and Robotics 3, no. 2 (April 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 estimation for improving the performance of robust adaptive filtering. Experiments and comparison analysis demonstrate that the proposed filtering algorithm can effectively resist disturbances due to system state noise and observation noise, leading to the improved filtering accuracy.

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25

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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26

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

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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 adjustment rate to adjust the integral term of PID control algorithm. Finally, this paper puts the improved adaptive filtering and anti-windup variable-structure PID control technique into combination to effectively restrain interference and integral saturation, so as to achieve the purpose of improving system stability and control precision. The simulation and experiment results show that methods described above greatly enhance the capabilities of restraining filtering divergence and improving control precision.

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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 using the mean filtering and the median filtering under bigger windows .

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29

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 (March 15, 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 the channels can be grouped in the form of rank-1 tensors. Nevertheless, the approach could be further extended for single-input/single-output system identification scenarios, where the impulse responses (of more general forms) can be modeled as higher-rank tensors. As compared to the conventional adaptive filters, which involve a single (usually long) filter for the estimation of the global impulse response, the tensor-based algorithms achieve faster convergence rate and tracking, while also providing better accuracy of the solution. Simulation results support the theoretical findings and indicate the advantages of the tensor-based algorithms over the conventional ones, in terms of the main performance criteria.

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30

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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31

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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32

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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33

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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34

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 (March 1996): 255–65. http://dx.doi.org/10.1109/82.486471.

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35

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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36

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

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37

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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38

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

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39

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

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40

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

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41

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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42

Diniz, Paulo S. R. "On Data-Selective Adaptive Filtering." IEEE Transactions on Signal Processing 66, no. 16 (August 15, 2018): 4239–52. http://dx.doi.org/10.1109/tsp.2018.2847657.

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43

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

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44

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

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45

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

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46

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

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47

Halme, A., J. Selkäinaho, and J. Soininen. "Adaptive control with nonlinear filtering." Automatica 21, no. 4 (July 1985): 453–63. http://dx.doi.org/10.1016/0005-1098(85)90081-0.

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48

Sun, Guangrui, and Julian A. Domaradzki. "Implicit LES using adaptive filtering." Journal of Computational Physics 359 (April 2018): 380–408. http://dx.doi.org/10.1016/j.jcp.2018.01.009.

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49

Yang, G. Z., P. Burger, D. N. Firmin, and S. R. Underwood. "Structure adaptive anisotropic image filtering." Image and Vision Computing 14, no. 2 (March 1996): 135–45. http://dx.doi.org/10.1016/0262-8856(95)01047-5.

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50

Che Ujang, Bukhari, Clive Cheong Took, and Danilo P. Mandic. "Quaternion-Valued Nonlinear Adaptive Filtering." IEEE Transactions on Neural Networks 22, no. 8 (August 2011): 1193–206. http://dx.doi.org/10.1109/tnn.2011.2157358.

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Journal articles on the topic 'Adaptive filtering'

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