• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.12
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• pp.969-975
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• 2018

The main function of the tracking radar is to automatically track the target. The amplitude-comparison monopulse radar utilizes a monopulse radar to estimate the angular components of a target. In this paper, the operating performance of the amplitude-comparison monopulse radar is quantitatively analyzed via the MSEs, with considerations on additive noise. The performance of the amplitude comparison monopulse radar can be predicted by comparing it with an approximated estimate.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.11
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• pp.1054-1062
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• 2011

This study describes the time domain imaging algorithm which can be well applied to short-range UWB(ultra wideband) bistatic radar. In the imaging method of SAR technology, the frequency domain method is well applied to the areas which satisfy far-field condition. However in the near-field environment, the image quality is not good due to phase error. However back-projection method based on time domain is well applied to short-range imaging radar. Meanwhile because its processing time is very long, real time-processing is very difficult. To resolve this problem FFBP(Fast Factorized Back-Projection) was proposed. Using the raw data gathered on field we implemented back-projection and FFBP method. Then image quality and processing time were analyzed using these methods.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.12
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• pp.103-110
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• 2005

The performance of maximum likelihood(ML) detection for the given channel is analyzed in spatially multiplexed MIMO system. In order to obtain the vector symbol error rate, we define error vectors which represent the geometrical relation between lattice points. The properties of error vectors are analyzed to show that all lattice points in infinite lattice almost surely have four nearest neighbors after random channel transformation. Using this information and minimum distance obtained by the modified sphere decoding algorithm, we formulate the analytical performance of vector symbol error over the given channel. To verify the result, we simulate ML performance over various random channel which are classified into three categories: unitary channel, dense channel, and sparse channel. From the simulation results, it is verified that the derived analytical result gives a good approximation about the performance of ML detector over the all random MIMO channels.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.9
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• pp.1759-1771
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• 1994

We proposed the iterative image restoration method based on the method of steepest descent with a regularization constraint for restoring the noisy motion-blurred images. The conventional method proposed by Jan Biemond et al, had drawback to amplify the additive noise and make ringing effects in the restored images by determining the value of regularization parameter experimentally from the degraded image to be restored without considering local information of the restored one. The method we proposed had a merit to suppress the noise amplification and restoration error by using the regularization parameter which estimate the value of it adaptively from each pixels of the image being restored in order to reduce the noise amplification and ringing effects efficiently. Also we proposed the termination rule to stop the iteration automatically when restored results approach into or diverse from the original solution in satisfaction. Through the experiments, proposed method showed better result not only in a MSE of 196 and 453 but also in the suppression of the noise amplification in the flat region compared with those proposed by Jan Biemond et al. of which MSE of 216 and 467 respectively when we used 'Lean' and 'Jaguar' images as original images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.17-30
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• 2000

In this paper, we propose an efficient algorithm to produce 3-D surface model from a set of range data, based on NURBS (Non-Uniform Rational B-Splines) surface fitting technique. It is assumed that the range data is initially unorganized and scattered 3-D points, while their connectivity is also unknown. The proposed algorithm consists of three steps: initial model approximation, hierarchical representation, and construction of the NURBS patch network. The mitral model is approximated by polyhedral and triangular model using K-means clustering technique Then, the initial model is represented by hierarchically decomposed tree structure. Based on this, $G^1$ continuous NURBS patch network is constructed efficiently. The computational complexity as well as the modeling error is much reduced by means of hierarchical decomposition and precise approximation of the NURBS control mesh Experimental results show that the initial model as well as the NURBS patch network are constructed automatically, while the modeling error is observed to be negligible.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.355-367
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• 2007

This paper proposes a design method of the PI(Proportional-Integral)+D(Derivative) controller using self-tuning of the design variables and controller gains. The used fuzzy PI+D controller is the approximated conventional continuos time linear PI+D controller and the used fuzzification method is the fuzzy single tone and the adapted defuzzification method is the simplified tenter of gravity. Fuzzy estimation result would be calculated in the other function elements from the classified fuzzy variables and the result determined by the design variables decides the controller gains. As a result, the proposed method shows the capability of the high speed tuning and can be applied to the case of input variables with many fuzzy partitions and also can bring out the advantage to reduce the reconstruction(digital sampling reconstruction) error. Most simulation results show that this controller makes much bettor efficiency and improvement by using design variables and controller gains.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.7
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• pp.32-37
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• 2008

Satellite communication systems including the DVB-S2 (Digital Video Broadcasting - Satellite Version 2) system require operations under low signal-to-noise ratio (SNR) and large frequency offset values, and the initial frame synchronization process necessitates a robust correlation method. While a variety of conventional correlation structures exist for the initial synchronization, each method has different characteristics and performance in different channel environments. In this paper, we propose new correlation methods which exhibit enhanced performance in low SNR and large frequency offsets, and analyze their performance. The proposed methods use the magnitude sum and vector sum of extended differential correlation values, to maximize the correlation between the received signal and the synchronization sequence by using the spanned differential correlation result. The magnitude sum correlation method has better performance compared to conventional methods including the approximated ML (Maximum likelihood) method for SNR values below 4 dB with or without frequency offsets. The vector sum correlation method has improved performance over the magnitude sum method for channels with relatively small frequency offsets.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.187-192
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• 2008

A saturation and deadzone compensator is designed for robot systems using fuzzy logic (FL) and neural network (NN). The classification property of FL system and the function approximation ability of the NN make them the natural candidate for the rejection of errors induced by the saturation and deadzone. The tuning algorithms are given for the fuzzy logic parameters and the NN weights, so that the saturation and deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The NN saturation and FL deadzone compensator is simulated on a robot system to show its efficacy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6484-6487
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• 2013

This paper reports the direct controller for nonlinear plants using a neural network. The controller was composed of an approximate controller and a neural network auxiliary controller. The approximate controller provides rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not place too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network was trained and the system showed stable performance for the inputs it has been trained for. The simulation results showed that it was quite effective and could realize satisfactory control of the nonlinear system.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.33-41
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• 2002

In this paper, we present a frequency weighted model reduction using LMIs for minimizing the H$\infty$ weighted model error compared with the methods of frequency weighted balanced truncation and frequency weighted Hankel norm approximation. The proposed algorithm, its form is equal to the sufficient condition of performance preserving controller approximation, is based on an iterative two-step LMI scheme induced from bound real lemma. So, it can be applied to the problem of the performance preserving controller approximation. The controller reduction is useful in a practical control design and ensures its easy implementation and high reliability The validity of the proposed algorithm is shown through numerical examples. Additionaly, we extend the proposed algorithm to performance preserving controller approximation by applying to the HIMAT(highly maneuverable aircraft technology) system.