• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.27-33
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• 2013

In this paper, we propose a pilot based channel estimation technique in two-way relay networks. We propose to transmit a pilot symbol together with the data symbol during transmission. In absence of Channel State Information (CSI), destination uses the pilot symbol to estimate the channel. In this system, the relay amplifies the pilot and the data symbol then forward them to the destination using amplify and forward (AF) protocol. We assume that the relay gain is fixed, so the relay does not need to estimate the channel, the destination only estimate the channel. We apply well-known Least-square (LS) and minimum mean-square error (MMSE) channel estimation methods to estimate the channel.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1715-1718
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• 2003

본 논문에서 우리는 정규화 된 혼합 노름(norm)을 이용한 다중 채널 영상 복원 알고리즘을 제안한다. 채널 내부와 채널 사이의 결정론적 정보를 이용하는 다중채널 복원 문제를 고려한다. 각 채널에서, LMS(Least Mean Square), LMF(Least Mean Fourth), 평탄 함수가 결합된 함수가 제안되었다. LMS와 LMF 사이의 적절한 분배를 제어하는 혼합 노를 매개변수와 해의 평탄 정도를 정의하는 정규화 매개 변수를 소개하며, 두 매개 변수는 각 채널의 잡음 특성에 따라 매번 반복적으로 갱신된다. 제안된 알고리즘은 각 채널의 잡음분포에 대한 지식이 필요하지 앉고 앞에서 언급된 매개 변수는 부분적으로 복원된 영상에 기반을 두고 조절하게 된다.

• Journal of Communications and Networks
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• v.9 no.1
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• pp.18-27
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• 2007

The conventional normalized least mean square (NLMS) algorithm is the most widely used for adaptive identification within a non-stationary input context. The convergence of the NLMS algorithm is independent of environmental changes. However, its steady state performance is impaired during input sequences with low dynamics. In this paper, we propose a new NLMS algorithm which is, in the steady state, insensitive to the time variations of the input dynamics. The square soot (SR)-NLMS algorithm is based on a normalization of the LMS adaptive filter input by the Euclidean norm of the tap-input. The tap-input power of the SR-NLMS adaptive filter is then equal to one even during sequences with low dynamics. Therefore, the amplification of the observation noise power by the tap-input power is cancelled in the misadjustment time evolution. The harmful effect of the low dynamics input sequences, on the steady state performance of the LMS adaptive filter are then reduced. In addition, the square root normalized input is more stationary than the base input. Therefore, the robustness of LMS adaptive filter with respect to the input non stationarity is enhanced. A performance analysis of the first- and the second-order statistic behavior of the proposed SR-NLMS adaptive filter is carried out. In particular, an analytical expression of the step size ensuring stability and mean convergence is derived. In addition, the results of an experimental study demonstrating the good performance of the SR-NLMS algorithm are given. A comparison of these results with those obtained from a standard NLMS algorithm, is performed. It is shown that, within a non-stationary input context, the SR-NLMS algorithm exhibits better performance than the NLMS algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.3
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• pp.288-295
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• 2013

In this paper, we compare the existing channel estimation technique for 4S (Ship to Ship, Ship to Shore) maritime communications under AWGN channel model, Rician fading channel model, and Rayleigh fading channel model respectively. In general, the received signal is corrupted by multipath and ISI (Inter Symbol Interference). The estimation of a time-varying multipath fading channel is a difficult task for the receiver. Its performance can be improved if an appropriate channel estimation filter is used. The simulation is performed in MATLAB. In this simulation, we use the popular estimation algorithms, LMS (Least Mean Square) and RLS (Recursive Least-Squares) are compared with respect to AWGN, Rician and Rayleigh channels.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.23-27
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• 1991

RLS(Recursive Least Squares)나 LMS(Least mean square)등은 알고리듬 고유의 성질상 잡음이 섞인 시스템에 있어서는 올바른 역 모델링을 할 수 없다. 따라서, 잡음의 영향을 받지않는 견실한(robust) 모델 추정 알고리듬이 필요하다. 본 논문에서는 잡음환경하에 있는 시스템을역 모델링하는데 있어서, 잡음의 영향을 줄이기위해 완전최소자승법을 도입하고 기존의 최소자승법과 비교 실험하였다. 그리고, 이 방법의 적응 알고리듬을 제안하였으며, RLS(Recursive least squares)와 그 성능을 비교하여 타당성을 검토하였다.

• Journal of Cadastre & Land InformatiX
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• v.45 no.2
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• pp.117-130
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• 2015

A least squares method for adjusting the horizontal network satisfies the conditions which is minimizing the sum of the squares of errors based on probability theory. This research compared accuracy of 3rd cadastral control points adjusted by traditional and least square method with respect to the result of Network-RTK. Test results showed the least square method more evenly distribute closure error than traditional method. Mean errors of least square and traditional adjusting method are 2.7cm, 2.2cm respectively. In addition, blunder in angle observations can be detected by comparing position errors which calculated by forward and backward initial coordinates. However, distance blunder cannot offer specific observation line occurred mistake because distance error propagates several observation lines which have similar directions.

• 전자공학회논문지 IE
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• v.45 no.1
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• pp.38-43
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• 2008

This paper presents a new interference cancellation method to remove the oscillation due to feedback signal of RF relay. In this paper, we estimate the feedback signal using to LMS(Least Mean Square) algorithm and remove the interference through attenuation signal arbitrary corresponding to feedback signal. From the various performance analysis for various doppler effects, a proposed method prevents from oscillation using -30dB attenuation signal as a cancellation value of feedback signal.

• Proceedings of the Acoustical Society of Korea Conference
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• 1985.10a
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• pp.87-94
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• 1985

In this paper, enhancement of speech corrupted by additive white or colored noise is stuided. The nuconstrained frequency-domain block least-mean-square (UFBLMS) adaptation algorithm and its frequency-weighted version are newly applied to speech enhancement. For enhancement of speech degraded by white noise, the performance of the UFBLMS algorithm is superior to the spectral subtraction method or Wiener filtering technique by more than 3 dB in segmented frequency-weighted signal-to-noise ratio(FWSNERSEG) when SNR of speech is in the range of 0 to 10 dB. As for enhancement of noisy speech corrupted by colored noise, the UFBLMS algorithm is superior to that of the spectral subtraction method by about 3 to 5 dB in FWSNRSEG. Also, it yields better performance by about 2 dB in FWSNR and FWSNRSEG than that of time-domain least-mean-square (TLMS) adaptive prediction filter(APF). In view of the computational complexity and performance improvement in speech quality and intelligibility, the frequency-weighted UFBLMS algorithm appears to yield the best performance among various algorithms in enhancing noisy speech corrupted by white or colored noise.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.153-155
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• 2004

This paper proposes a real-time approach on the rotational motion estimation and correction for the roll stabilization of the sight system. This method first estimates a rotation center by the least-mean square algorithm based on the motion vectors of some feature points. And, then, a rotation angle is searched for a best matching block between a reference block image and seccessive input images using MPC(maximum pixel count) matching criterion. Finally, motion correction is performed by the bilinear interpolation technique. Various computer simulations show that the estimation performance is good and the proposed algorithm is a real-time implementable one to the TMS320C6415(500MHz) DSP.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.565-567
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• 2004

In active noise control (ANC) systems, the convergence behavior of the conventional Filtered-X Least Mean Square (FXLMS) algorithm may be affected by nonlinear distortions in the secondary path (e.g., in the power amplifiers, loudspeakers, transducers, etc.), which may lead to degradation of the error-reduction performance of the ANC systems. In this paper, a stable FXLMS algorithm with fast convergence is proposed to compensate for undesirable nonlinear distortions in the secondary-path of ANC systems by employing the Volterra filtering approach. In particular, the proposed approach is based on the utilization of the conventional P-th order inverse approach to nonlinearity compensation in the secondary path of ANC systems. Finally, the simulation results showed that the proposed approach yields a better convergence behavior In the nonlinear ANC systems than the conventional FXLMS.