• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.151-156
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• 1991

In this paper, we propose a new algorithm for designing the R-G filter that has optimum performance in terms of mean square sidelobe level(MSSL) for the Barker code. The advantage of the conventional R-G filter lies in its simple structure so that it can be easily implemented. However, the conventional R-G filter dose not have optimum performances in terms of peak sidelobe level(PSL), mean sidelobe level(MSL), and MSSL. Recently, a(R-G)LP filter of which filter coefficients are obtained by the linear programming algorithm was proposed and known to have optimum performance in PSL. The proposed (R-G)LS filter keeps the simple structure of the conventional R-G filter and has the filter coefficients that minimizes the sidelobe in the least square sense. The analytic results show that the proposed (R-G)LS filter has better performances than the conventional R-G filter in terms of PSL, MSL, and MSSL. Compared with (R-G)LP filter, the proposed (R-G)LS filter has better performances in terms of MSL and MSSL. The proposed filter design algorithm can be applied to the other binary codes such as truncated pseudonoise(PN) codes and concatenated codes.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.565-569
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• 2003

In this paper, a robust filter is proposed to effectively estimate the system states in the case where system model uncertainties as well as disturbances are present. The proposed robust filter is constructed based on the linear approximation methods for a general nonlinear uncertain system with an integral quadratic constraint. We also derive the important characteristic of the proposed filter, a modified $H_{\infty}$ performance index. Analysis results show that the proposed filter has robustness against disturbances, such as process and measurement noises, and against parameter uncertainties. Simulation results show that the proposed filter effectively improves the performance.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.955-958
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• 1998

This paper describes the algorithm which extracts moving vehicles from sequential images and tracks those vehicles using Kalman filter. This work is composed of a motion segmentation stage which extracts moving objects from sequential images and gets features of objects, and a motion estimation stage which estimates the position and the motion of moving objects using Kalman filter. In the motion estimation stage, applying to affine motion model we divided the Kalman filter into position filter and velocity filter to employ linear Kalman filter. Multi-target tracking requires a data association component that decides which measurement to use for updating the state of which object. We use pattern recognition method to solve this problem.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.125-130
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• 2011

This paper shows a method to design complementary filter using least square. The complementary filter is one of useful filters estimating angle. The basic concept of this filter is to enhance advantages of each sensor that angle detecting using a gyroscope has good accuracy at a high frequency and an accelerometer at a low frequency. When designing complementary filter, the most commonly used method is using cut-off frequency. However, it may be not easy to obtain a cut-off frequency. This paper presents a systematic method to determine the coefficients of the complementary filter using well-known linear least squares minimizing error between estimating angle and true angle.

• Journal of the Optical Society of Korea
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• v.20 no.3
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• pp.368-380
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• 2016

There is no doubt that adaptive optics (AO) is the most promising method to compensate wavefront disturbance in free space optics communication (FSO). In order to improve the performance of the AO system described by discrete-time linear system model with time-delay and implicit phase turbulent model, new controllers based on a Kalman filter and its extensions are proposed. Based on the standard Kalman filter, we propose a fading memory filter to deal with the ruleless strong interference; sequential and U-D filters are applied to reduce implementation complexity for the embedded controllers. Theoretical analysis and the numerical simulations show that the proposed fading memory filter can upgrade the performance for AO systems in consideration of the unforeseen strong pulse interference, and the sequential and U-D filters perform well compared with a Kalman filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.5
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• pp.963-969
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• 2000

In this paper, we define two input-output gains of linear periodic time-varying systems. One is the ratio of output with worst-case l2-norm over all inputs with unit 12-norm. It denotes G($\iota_2,\iota_2$.The other is the ratio of output with worst-case RMS value over all inputs with unit RMS value. It denotes G(RMS, RMS) .It is fact that these two gains are equivalent for linear time-invariant system. In this paper, we prove these two gains are also equivalent for linear periodic time-varying system. In addition, the relationship between two method of obtaining the generalized frequency responses for linear periodic time-varying system is derived. Finally, we apply the defined input-output gains to M-channel filter-bank which is multi-rate signal Processing system, used to speech coding. In the filter-bank, generally, aliasing distortion, magnitude distortion, and phase distortion are present. It is shown that these are kept small if the filter-bank is designed by a method that optimizes the gain G($\iota_2,\iota_2$ of an error system.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.470-472
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• 1987

This paper deals with the continuous-discrete estimation problem using FIR filters and performs modeling error analyses of the FIR filters, compared to Kalman filter and the limited memory filters, via computer simulations. It is shown that, the less driving noise the system has, the better performance the FIR filter exhibits and that this characteristic appears rare distinctly in nonlinear system than in linear systems.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1854-1855
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• 2006

In this paper, the Takagi-Sugeno (TS) fuzzy state estimation scheme, which is suggested for a steady state estimator using standard Kalman filter theory with uncertainties. In that case, the steady state with uncertain can be represented by the TS fuzzy model structure, which is further rearranged to give a set of uncertain linear model using standard Kalman filter theory. And then the unknown uncertainty is regarded as an additive process noise. To optimize fuzzy system, we utilize the genetic algorithm. The steady state solutions can be found for proposed linear model then the linear combination is used to derive a global model. The proposed state estimator is demonstrated on a truck-trailer.

• Proceedings of the KIEE Conference
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• 2003.07e
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• pp.87-91
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• 2003

Most of the loads in industrial power distribution systems are balanced and connected to three wires power systems. However, in the user power distribution systems, most of the loads are single & three phase and unbalanced, generating a large amount of non-characteristic harmonics. With the advent of power electronics and proliferation of non-linear loads in industrial power applications, power harmonics and their effects on power quality are a topic of concern. Harmonics by the unbalance voltage and non-linear loads, cause the increase of machine loss and heating. In order to allow current harmonic compensation, a filter must be installed. This paper describes the performance of passive filter under the voltage unbalance and non-linear load.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1041-1044
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• 1988

This paper considers the design problem of adaptive filters based an the state-space models for linear discrete-time stationary stochastic signal processes. The adaptive state estimator consists of both the predictor and the sequential prediction error estimator. The discrete Chandrasakhar filter developed by author is employed as the predictor and the nonlinear least-squares estimator is used as the sequential prediction error estimator. Two models are presented for calculating the parameter sensitivity functions in the adaptive filter. One is the exact model called the linear innovations model and the other is the simplified model obtained by neglecting the sensitivities of the Chandrasekhar X and Y functions with respect to the unknown parameters in the exact model.