• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.80-80
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• 2000

The GPS can provide accurate position information on the earth. But GPS receiver can't give position information inside buildings. DR(Dead-Reckoning) or INS(Inertial Navigation System) gives position information continuously indoors as well as outdoors, because they do not depend on the external navigation information. But in general, the inertial sensors severely suffer from their drift errors, the error of these navigation system increases with time. GPS and DR sensors can be integrated together with Kalman filter to overcome these problems. In this paper, we developed a personal navigation system which can be carried by person, using GPS and electronic pedometer. The person's footstep is detected by an accelerometer installed in vertical direction and the direction of movement is sensed by gyroscope and magnetic compass. In this case the step size is varying with person and changing with circumstance, so determining step size is the problem. In order to calculate the step size of detected footstep, the neural network method is used. The teaming pattern of the neural network is determined by human walking pattern data provided by 3-axis accelerometer and gyroscope. We can calculate person's location with displacement and heading from this information. And this neural network method that calculates step size gives more improved position information better than fixed step size.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.4147-4152
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• 2011

Compared to the constant modulus algorithm (CMA), the modified CMA (MCMA) is easy not only to improve the steady-state performance but also to be expanded to higher-order constellations by using fewer moduli with evenly spaced. In this paper, it is shown that the MCMA is sufficient to achieve satisfactory steady-state performance by applying a variable step-size to the MCMA without switching to an hard decision-directed algorithm. Two new methods varying the step-size are proposed, and the performance improvement of the MCMA with the new methods of variable step-size is presented as compared to the CMA and the fixed step-size MCMA through computer simulations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.27-32
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• 2021

This paper evaluates the equalization performance of FC-MMA adaptive equalization algorithm by the fixed step size that is used for the minimization of the intersymbol interference which occurs in the time dispersive communication channel. The FC-MMA has a fast convergence speed in order to adapts the new environment more rapidly in case of the time varying charateristics and the abnormal situation like as outage of the communication channel. But the algorithms operates in adative method, convegence speed is depend on fixed step size for adaptation. For this situation, its performance was evaluated by changing the step size value, the residual isi and maximum distortion and MSE performance index which means the convergence characteristics are widely adapted in the adaptive equalizer, SER were applied. As a result of computer simulation, the large step size can improves the convergence speed for reaching the steady state, but has a poor performance compared to small step size in residual values after steady state. The research result shows that the FC-MMA algorithm is applied the large step size for rapidly reaching the steady state in initial time, then adjust the small step size after reaching the steady state for reducing the residual values for equalization.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1854-1857
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• 1991

A numerically efficient modification of a variable step size LMS (Least Mean Squares) algorithm is proposed. This proposed algorithm is very simple in calculation and has a variable step size adjusted by the filter output error. Its additional computational burden with respect to the conventional LMS algorithm is only two multiplications, two substraction, an addition and some bit operations. In a simulation example, it is shown that the proposed algorithm has not only the faster convergence rate but also less misadjustments in the environment of highly nonstationary and correlated data.

• The Journal of the Acoustical Society of Korea
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• v.33 no.5
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• pp.309-315
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• 2014

In this paper, a novel variable step-size filtered-x gradient adaptive lattice (NVSS-FxGAL) algorithm for active noise control system is proposed. The gradient adaptive lattice (GAL) algorithm is capable of controlling the narrow band noise effectively. The GAL algorithm can achieve both fast convergence rate and low steady-state level using the variable step-size. However, it suffers from the convergence performance for varying signal characteristic since the global variable step-size is equally applied to all lattice stages. Therefore, the proposed algorithm guarantees the stable and consistency convergence performance by using the local variable step-size for the suitable each lattice stage. Simulation results confirm that the proposed algorithm can obtain the fast convergence rate and low steady-state level compared to the conventional algorithms.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.7
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• pp.569-581
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• 1989

This paper deals with a tracking algorithm based on integral projection which tracks moving targets with varying brightness and size. An adaptive windowing technique is employed to reduce the sensitivity of the system to the complex background image and also to reduce the computational load. The threshold value is determined by considering both the size and the threshold value of the brightness intensity of the recognized target obtained in the previous processing step. Window position is estimated by using the information of the velocity and acceleration of the target. And integral projection is applied to find the position of the target in the window accurately. Experimental results show that moving targets with varying brightness and size can be tracked properly in noisy environments.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.440-444
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• 2007

As the mobile communication service is widely used, the demand for wireless repeaters is rapidly increasing because of the easiness of extending service areas. But a wireless repeater has a problem that the output of the transmit antenna is partially fed back to the receive antenna, which results in feedback interference. In this paper, we propose a new varable step-size LMS algorithm, which utilizes correlation between reference and error signals to adjust the step sizes, for cancelling the feedback interference signals in the WCDMA repeater under time-varying multi-path channels. The proposed algorithm was investigated through computer simualation by being applied to the time-varying channels. The simulation results demonstrated that the proposed one is superior to the conventional ones in terms of cancelation perormance.

• The Journal of the Acoustical Society of Korea
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• v.34 no.5
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• pp.394-402
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• 2015

CMA (Constant Modulus Algorithm) is one of the well-known algorithms in blind acoustic channel equalization. Generally, CMA converges slowly and the speed of convergence is dependent on a step-size in the CMA procedure. Many researches have tried to speed up the convergence speed by applying a variable step-size to CMA, e.g. the orthogonal projection CMA and algebraic optimal step-size CMA. In this paper, we summarize these two algorithms, and we propose a new CMA with improved convergence performance. The improvement comes from the switching between the orthogonal projection CMA and algebraic optimal step-size CMA. In simulation results, we show the performance improvement in the time invariant channels as well as in time varying channel.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.49-55
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• 2020

Information theoretic learning (ITL) methods based on random symbols (RS) use a set of random symbols generated according to a target distribution and are designed nonparametrically to minimize the cost function of the Euclidian distance between the target distribution and the input distribution. One drawback of the learning method is that it can not utilize the input power statistics by employing a constant stepsize for updating the algorithm. In this paper, it is revealed that firstly, information potential input (IPI) plays a role of input in the cost function-derivative related with information potential output (IPO) and secondly, input itself does in the derivative related with information potential error (IPE). Based on these observations, it is proposed to normalize the step-size with the statistically varying power of the two different inputs, IPI and input itself. The proposed algorithm in an communication environment of impulsive noise and multipath fading shows that the performance of mean squared error (MSE) is lower by 4dB, and convergence speed is 2 times faster than the conventional methods without step-size normalization.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.67-72
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• 2019

This paper evaluates the performance of CCA (Compact Constellation Algorithm) adaptive equalization algorithm by varying the step size for minimization of the distortion effect in the communication channel. The CCA combines the conventional DDA and RCA algorithm, it uses the constant modulus of the transmission signal and the considering the output of decision device by the power of compact slice weighting value in order to improving the initial convergence characteristics and the equalization noise by misadjustment in the steady state. In this process, the compact slice weight values were fixed, and the performance of CCA adaptive equalization algorithm was evaluated by the varing the three values of step size for adaptation. As a result of computer simulation, it shows that the smaller step size gives slow convergence speed, but gives excellent performance after at steady state. Especially in SER performance, the small step size gives more robustness that large values.