• Journal of IKEEE
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• v.17 no.2
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• pp.111-118
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• 2013

In this paper, the subband nonlinear adaptive Volterra filters are introduced and its analysis are presented. From the eigenvalue analysis of the input correlation matrix, we show that the proposed subband adaptive Volterra filter has superior convergence performance as compared to the conventional one, which shows that the it can be useful for the recently proposed subband nonlinear adaptive echo canceller. Also, the optimum filter in each subband are introduced and verified from the computer simulations.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.45-55
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• 1999

In this paper, we used the WLMS(Wavelet domain Least Mean Square) adaptive filter based on the wavelet transform to cancel grain noise. Usually, grain noise occurs in changes of the crystalline structure of metals in high temperature environment. It makes the detection of flaw difficult. The WLMS adaptive filtering algorithm establishes the faster convergence rate by orthogonalizaing the input vector of adaptive filter as compared with that of LMS adaptive filtering algorithm in time domain. We implemented the WLMS adaptive filter by using the delayed version of the primary input vector as the reference input vector and then implemented the CA-CFAR(Cell Averaging- Constant False Alarm Rate) threshold estimator. CA-CFAR threshold estimator enables to detect the flaw and back echo signals automatically. Here, we used the output signals of adaptive filter as its input signal. To Cow the statistical characteristic of ultrasonic signals corrupted by grain noise, we performed run test. The results showed that ultrasonic signals are nonstationary signal, that is, signals whose statistical properties vary with time. The performance of each filter is appreciated by the signal-to-noise ratio. After LMS adaptive filtering in time domain, SNR improves to about 2-3㏈ but after WLMS adaptive filtering in wavelet domain, SNR improves to about 4-6㏈.

• Speech Sciences
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• v.9 no.4
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• pp.275-287
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• 2002

In man-machine communication by speech under the noisy environment, the quality of speech may be degraded severely for the machine to recognize correctly. Especially when the corrupting noise occupies the same band as the speech, the conventional fixed filters cannot filter out the noise effectively. In recent, to resolve such a problem adaptive noise canceller (ANC) is frequently used, which is based upon adaptive filters. The Adaptive recursive filters perform better than adaptive nonrecursive filters due to the added poles, but the stability may be severely threatened. In this paper an ANC system employing the adaptive recursive filter is proposed to enhance the speech corrupted by noise. And the stability of the adaptive recursive filter is guaranteed by employing the adaptive compensator.

• Journal of Korea Multimedia Society
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• v.12 no.1
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• pp.26-30
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• 2009

We propose an adaptive Recursive Least Rank(RLR) L-filter which uses an L-estimator in order statistics and is based on rank estimate in robust statistics. The proposed RLR L-filter is a non-linear adaptive filter using non-linear adaptive algorithm and adapts itself to optimal filter in the sense of least dispersion measure of errors with non-homogeneous step size. Therefore the filter may be suitable for applications when the transmission channel is nonlinear channels such as Gaussian noise or impulsive noise, or when the signal is non-stationary such as image signal.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.947-953
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• 2011

Radome aberration error causes degradation of miss distance as well as stability of high maneuver missile system with RF seeker. A study about radome compensation method is important in this kind of missile system design. Several kinds of methods showed good compensation performance in their paper. Proposed adaptive Particle filter estimates line of sight rate excluding the radome induced error. This paper shows effectiveness of adaptive Particle filter as compensation method of radome aberration error. Robust performance of this filter depends on external aiding measurement, target acceleration. Tuning of system error covariance can make this filter unsensitive against the error of target acceleration information. This paper demonstrates practical usage of adaptive Particle filter for reducing miss distance and increasing stability against disturbance of radome aberration error through performance analysis.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3E
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• pp.95-103
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• 2008

In this paper, the new hybrid adaptive Volterra filter was proposed to be applied for compensating the nonlinear distortion of memoryless nonlinear systems with saturation characteristics. Through computer simulations as well as the analytical analysis, it could be shown that it is possible for both conventional Volterra filter and proposed hybrid Volterra filter, to be applied for linearizing the memoryless nonlinear system with nonlinear distortion. Also, the simulations results demonstrated that the proposed hybrid filter may have faster convergence speed and better capability of compensating the nonlinear distortion than the conventional Volterra filter.

• Journal of IKEEE
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• v.21 no.3
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• pp.202-210
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• 2017

Recently, some variable length adaptive filters which employ variable lengths taps for the input signal statistics are proposed [1-5]. In this paper, a new subband adaptive filter with variable filter tap length is proposed. The proposed subband variable length adaptive filters can optimize filter length for each subband which can result less computational complexities with respect to the conventional full band adaptive filters. When the signal in the full band has narrow spectrum, the conventional full band adaptive requires very long filter taps, whereas the proposed subband variable filter requires less taps with the spectrum split in subband. The computer simulation results reveals that in many case, in system identification with narrow band system estimation, the proposed adaptive filter has less computational complexities with faster convergence.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.370-378
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• 2002

Fuzzy inference which combines numerical data and linguistic data has been used to design adaptive filter algorithms. In adaptive IIR filter design, the fuzzy prefilter is taken account, and applied to both direct and lattice structure. As for the fuzzy inference of the fuzzy filter, the Sugeno's method is employed. As membership functions and inference rules are recursively generated through neural network, the accuracy can be improved. The proposed adaptive algorithm, adaptive IIR filter with fuzzy prefilter, has been applied to adaptive system identification for the purposed of performance test. The evaluations have been carried out with viewpoints of convergence property and tracking properties of the parameter estimation. As a result, the faster convergence and the better coefficients tracking performance than those of the conventional algorithm are shown in case of direct structures.

• Journal of IKEEE
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• v.24 no.2
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• pp.570-578
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• 2020

In this paper, we propose an efficient method for implementing the adaptive second-order Volterra filter. To reduce computational load, the UCFD-SVF has been proposed. The UCFD-SVF, however, shows deteriorated convergence performance. We propose a new method that initializes the adaptive filter weights periodically on the fact that the energy of the filter weights is slowly increased. Furthermore, we propose another method that the interval for the weight initialization is variable to guarantee the performance and we shows the method gives the better performance under the non-stationary environment through the computer simulation for the adaptive system identification.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.295-303
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• 2008

In general, transform domain adaptive filters show faster convergence speed than the time domain adaptive filters, but the amount of calculation increases dramatically as the filter order increases. This problem can be solved by making use of the subband structure in transform domain adaptive filters. In this paper, to increase the convergence speed on the generalized subband decomposition FIR adaptive filters, a structure of the adaptive filter with subfilter of dyadic sparsity factor in wavelet transform domain is designed. And, in this adaptive filter, the equivalent input in transform domain is derived and, by using the input, the convergence properties for the LMS algorithm is analyzed and evaluated. By using this sub band adaptive filter, the inverse system modeling and the periodic noise canceller were designed, and, by computer simulation, the convergence speeds of the systems on LMS algorithm were compared with that of the subband adaptive filter using DFT(discrete Fourier transform).