• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.649-653
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• 2005

To estimate for an error signal with sigmoid nonlinearity what reduced constellation applies closed eye pattern in the initial equalization, there can be improves problems of previous soft decision-directed algorithm that increasing estimate complexity and decreasing of convergence speed when substitute high-order constellation. The characteristic of sigmoid function is adjusted by a mean and a variance parameter, so it depends on adjustment of variance that what reduced constellation $values(\gamma)$ can have ranges between + $\gamma$ and - $\gamma$. In this paper, we proposed Variable Modulus Algorithm (VMA) that can be improving a performance of steady-state by adjustment of variance when equalization works normally and each cluster of constellation decrease.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.925-930
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• 1994

We consider statistical properties of the generalized Capon's method. It is observed that the estimation error of the generalized Capon's method has almost the same variance as the MUSIC method, although the generalized Capon's method yields a slightly biased estimate.

• The Journal of Natural Sciences
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• v.8 no.1
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• pp.5-15
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• 1995

The equation error formulation in the adaptive IIR filtering provides convergence to a global minimum regardless a local minimum with a large stability margin. However, the equation error formulation suffers from the bias in the coefficient estimates. In this paper, a new algorithm, which does not require a prespecification of the noise variance, is proposed for the equation error formulation. This algorithm is based on the equation error smoothing and provides an unbiased parameter estimate in the presence of white noise. Through simulations, it is demonstrated that the algorithm eliminates the bias in the parameter estimate while retaining good properties of the equation error formulation such as fast convergence speed and the large stability margin.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.11
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• pp.503-510
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• 1986

A new algorithm based on the concept of prediction error minimization is suggested to estimate the time varying delay in discrete processes. In spite of the existence of the stochastic noise, this algorithm can estimate time varying delay accurately. Computation time of this algorithm is far less than that of the previous extended parameter methods. With the use of this algorithm, generalized minimum variance control shows good control behavior in simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.420-424
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• 2003

A new adaptive filter is proposed for an aided strapdown inertial navigation system(SDINS). The proposed filter can be used to effectively estimate the time-varying variance of the measurement noise. Then, the in-flight alignment for the radar aided SDINS is designed using the additive quatermion error model. Simulation results show that the proposed adaptive filter effectively improves the performance of the radar aided SDINS.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.583-591
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• 2014

In wireless communications, knowledge of the signal-to-noise ratio is required in diverse communication applications. In this paper, we derive the variance of the maximum likelihood estimator in the data-aided and non-data-aided schemes for determining the optimal shrinkage factor. The shrinkage factor is usually the constant that is multiplied by the unbiased estimate and it increases the bias slightly while considerably decreasing the variance so that the overall mean squared error decreases. The closed-form biased estimators for binary-phase-shift-keying and quadrature phase-shift-keying systems are then obtained. Simulation results show that the mean squared error of the proposed method is lower than that of the maximum likelihood method for low and moderate signal-to-noise ratio conditions.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3100-3102
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• 2000

In this paper, we propose to a robust frequency offset estimation method of OFDM signals. A carrier frequency offset may be decomposed into an integer multiple of the subcarrier spacing and a residual frequency offset. Fractional part of frequency offset is obtained by using the maximum likelihood estimation(MLE) method. And we use the correlation of the samples at the output of the discrete Fourier transform(DFT) to estimate integer part of frequency offset. The result shows that the estimation frequency offset is almost linear to frequency offset. We propose to an improved estimation error variance of the carrier frequency offset estimation. The proposed estimator has better performance than the conventional ones in terms of error variance and tracking range.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.577-585
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• 2009

So far in a nonparametric regression model one of the interesting problems is estimating the error variance. In this paper we propose an estimator of the mean of the squared functions which is the numerator of SNR (Signal to Noise Ratio). To estimate SNR, the mean of the squared function should be firstly estimated. Our focus is on estimating the amplitude, that is the mean of the squared functions, in a nonparametric regression using a simple linear regression model with the quadratic form of observations as the dependent variable and the function of a lag as the regressor. Our method can be extended to nonparametric regression models with multivariate functions on unequally spaced design points or clustered designed points.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.277-286
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• 2010

In this paper amplitude and phase distortion of the received signal through a fading channel results in a severe performance degradation of the communication system, Therefore we consider the variance of the maximum a posteriori phase estimate of sinusoidal signal by the Cramer-Rao bound in wireless fading channel. To find the Cramer-Rao lower bound for the variance of the phase, We use the derived probability density function(pdf) of the phase in Nakagami fading channel. We analyze the error performance of modulation signals using order statistics on generalized combining reception and find adequate diversity branch number.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.996-1003
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• 1999

This paper propose a position-speed controller with an estimator which can estimate states and disturbance. The overall control system consists of two parts: the position-speed controller and an estimator. The Kalman filter applied as state-feedback controller is an optimal state estimator applied to a dynamic system that involves random perturbations and gives a linear, unbiased and minimum error variance recursive algorithm to optimally estimate the unknown state. Therefore, we consider the error problem about the servo system modeling and the measurement noise as a stochastic system and implement a optimal state observer, and enhance the estimate performance of position and speed using that. Using two-degree-of freedom(TDOF) conception, we design the command input response and the closed loop characteristics independently. The servo system is to improve the closed loop characteristics without affecting the command imput response. The characteristics of the closed loop system is improved by suppressing disturbance torque effectively with the disturbance observer using a inverse-transfer matrix. Therefore, the performance of overall position-speed controller is enhanced. Finally, the performance of the proposed controller is exemplified by some simulations and by applying the real servo system.