• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.2
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• pp.170-177
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• 1986

The adaptive array antenna with the LMS algorithm has the advantage in that it can perform train't because of its slower convergencerate. In this paper, the \ulcornerLMS algorithm is applied to the adaptive array so that the convergence rate can be improved, and the performance of he adaptive array by the \ulcornerLMS algorithm is compared to, that of the LMS adaptive array. It is shown that the adaptive array by the \ulcornerLMS algorithm is superior to the LMS adaptive array in the narrow frequency band.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.42-46
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• 2018

Adaptive signal processing is quite important in various signal and communication environments. In adaptive signal processing methods since the least mean square(LMS) algorithm is simple and robust, it is used everywhere. As the step is varied in the variable step(VS) LMS algorithm, the fast convergence speed and the small excess mean square error can be obtained. Various variable step LMS algorithms are researched for better performances. But in some of variable step LMS algorithms the computational complexity is quite large for better performances. The fixed step LMS algorithm with a low computational complexity merit and the variable step LMS algorithm with a fast convergence merit are combined in the proposed sporadic step algorithm. As the step is sporadically updated, the performances of the variable step LMS algorithm can be maintained in the low update rate using Fibonacci sequence. The performances of the proposed variable step LMS algorithm are proved in the adaptive equalizer.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.128-132
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• 2011

Least mean square adaptive algorithms have played important role in radar, sonar, speech processing, and mobile communication. In mobile communication area, the convergence rate of a LMS algorithm is quite important. However, LMS algorithms have slow and non-uniform convergence rate problem For overcoming these shortcomings, various variable step LMS adaptive algorithms have been studied in recent years. Most of these recent LMS algorithms have used complex variable step methods to get a rapid convergence. But complex variable step methods need a high computational complexity. Therefore, the main merits such as the simplicity and the robustness in a LMS algorithm can be eroded. The proposed deterministic variable step LMS algorithm is based upon a simple deterministic function for the step update so that the simplicity of the proposed algorithm is obtained and the fast convergence is still maintainable.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.11
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• pp.32-39
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• 2009

In this thesis, design effective elimination interference algorithm of ICS repeat system for repeater that improve frequency efficiency. Gennerally, LMS Algorithm apply to ICS repeat system. Error convergence speed and accuracy of LMS Algorithm are influenced by reference signal. For improve LMS Algorithm, suggest Adaptive searching channel estimate algorithm. For using channel characteristic, adaptive searching channel estimate algorithm make reference signal similar interference signal by convolution operation and complement LMS algorithm demerit. For make channel similar pratical channel, apply Jake's Rayleigh multi-path model. LMS algorithm and suggested adaptive searching channel estimate algorithm that have 16 taps apply to ICS repeat system under Rayleigh multi-path channel, so simulate with MATLAB. According to simulate, ICS repeat system with LMS algorithm show -40 dB mean square error convergent after 110 datas iteration and ICS repeat system with adaptive searching channel estimate algorithm show -80 dB mean square en-or convergent after 120 datas iteration. Analyze simulation result, suggested adaptive searching channel estimate algorithm show 40 dB accuracy than LMS algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.2
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• pp.146-155
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• 2012

The filtered-x LMS(FX-LMS) algorithm has been applied to the active noise control(ANC) system in an acoustic duct. This algorithm is designed based on the FIR(finite impulse response) filter, but it has a slow convergence problem because of a large number of zero coefficients. In order to improve the convergence performance, the step size of the LMS algorithm was modified from fixed to variable. However, this algorithm is still not suitable for the ANC system of a short acoustic duct since the reference signal is affected by the backward acoustic wave propagated from a secondary source. Therefore, the recursive filtered-u LMS algorithm(FU-LMS) based on infinite impulse response(IIR) is developed by considering the backward acoustic propagation. This algorithm, unfortunately, generally has a stability problem. The stability problem was improved by using an error smoothing filter. In this paper, the recursive LMS algorithm with variable step size and smoothing error filter is designed. This recursive LMS algorithm, called FU-VSSLMS algorithm, uses an IIR filter. With fast convergence and good stability, this algorithm is suitable for the ANC system in a short acoustic duct such as the intake system of an automotive. This algorithm is applied to the ANC system of a short acoustic duct. The disturbance signals used as primary noise source are a sinusoidal signal embedded in white noise and the chirp signal of which the instantaneous frequency is variable. Test results demonstrate that the FU-VSSLMS algorithm has superior convergence performance to the FX-LMS algorithm and FX-LMS algorithm. It is successfully applied to the ANC system in a short duct.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.224-227
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• 1993

In this paper, a new LMS algorithm with a variable step size (VVS LMS) is presented. The change of step size .mu. at each iteration, which increases or decreases according to the misadaptation degree, is computed by a proportional fuzzy logic controller. As a result the algorithm has very good convergence speed and low steady-state misadjustment. The norm of the cross correlation between the estimation error and input signal is used. As a measure of the misadaptation degree. Simulation results are presented to compare the performance of the VSS LMS algorithm with the normalized LMS algorithm.

• Journal of Industrial Technology
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• v.13
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• pp.33-41
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• 1993

In this paper, a new LMS algorithm with a fuzzy variable step size (FVS LMS) is presented. The change of step size ${\mu}$, at each iteration which is increases or decreases according to the misadaptation degree, is computed by a proportional fuzzy logic controller. As a result the algorithm has very good convergence speed and low steady-state misadjustment. As a measure of the misadaptation degree, the norm of the cross correlation between the estimation error and input signal is used. Simulation results are presented to compare the performance of the FVSS LMS algorithm with the normalized LMS algorithm.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.245-250
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• 2009

In communications and signal processing area, a number of least mean square adaptive algorithms have been used because of simplicity and robustness. However the LMS algorithm is known to have slow and non-uniform convergence. Various variable step size LMS adaptive algorithms have been introduced and researched to speed up the convergence rate. A variable step size LMS algorithm using the error difference for updating the step size is proposed. Compared with other algorithms, simulation results show that the proposed LMS algorithm has a fast convergence. The theoretical performance of the proposed algorithm is also analyzed for the steady state.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.549-554
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• 2014

In the paper, we propose a new Signed LMS(Least Mean Square) algorithm for ICS(Interference Cancellation System). The proposed Signed LMS algorithm improved performances by adjusting step size values. At the convergence of 1000 iteration state, the MSE(Mean Square Error) performance of the proposed Signed LMS algorithm with step size of 0.067 is about 3 ~ 18 dB better than the conventional LMS, CMA algorithm. And the proposed Signed LMS algorithm requires 500 ~ 4000 less iterations than the and LMS and CMA algorithms at MSE of -25dB.

• The Journal of the Acoustical Society of Korea
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• v.35 no.4
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• pp.280-287
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• 2016

In this paper, we propose a normalization algorithm that can be applied to adaptive filters for multi-channel active noise control. The FxLMS (Filtered-x Least Mean Square) algorithm for the single-channel active noise control can be normalized in the same way as the NLMS (Normalized Least Mean Square) algorithm, whereas in case of the multi-channel active noise control, the single-channel normalization for the FxLMS algorithm cannot be extended to the normalization for the multi-channel FxLMS algorithm straightforwardly. First, we adopt a generalized normalization algorithm for the multi-channel FxLMS algorithm based on the principle of minimal disturbance and then, proposed a normalized algorithm considering only diagonal elements to avoid computation for matrix inversion. We carried out performance comparisons of the proposed algorithm with other algorithms without normalization. It is shown that the proposed algorithm presents better convergence characteristics under non-stationary environments.