• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.1005-1006
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• 2006

This paper proposes a method of position estimating through compensating the autonomous mobile robot's noise. Proposed method is that estimated position error by modified Kalman filter method using neural network. We use a neural network for measurement noise covariance and system noise covariance. In order to verify the effectiveness of the proposed method, we performed experiments for position estimation. The results show that convergence and position error is reduced than the Kalman filter method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.3
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• pp.372-375
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• 2014

A monopulse based on maximum likelihood(ML) in jamming scenario can suppress jamming signal using an inverse matrix of a covariance matrix. In order to achieve adequate suppression of jamming signal, the sufficient number of snapshots is required. However, this is not possible in high PRF scenario, which hinders a real-time tracking. Moreover, even with the large number of snapshots, the estimation accuracy of the target direction is decreased in low JNR(Jammer to Noise Ratio) due to insufficient jammer suppression. In this paper, we propose a monopulse algorithm that doesn't degrade performance significantly with a small number of snapshots and in low JNR. We show its derivation that exploits noise-jammer subspace of a covariance matrix, along with its performance through simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5C
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• pp.527-539
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• 2009

In this paper, we propose an asynchonous ICI (Inter-Cell Interference) mitigation techniques for 3GPP LTE MIMO-OFDMA down-link receiver. An increasing in symbol timing misalignments may occur relative to sychronous network as the result of BS (Base Station) timing differences. Such symbol synchronization errors that exceed the guard interval or the cyclic prefix duration may result in MAI (Multiple Access Interference) for other carriers. In particular, at the cell boundary, this MAI becomes a critical factor, leading to degraded channel throughput and severe asynchronous ICI. Hence, many researchers have investigated the interference mitigation method in the presence of asynchronous ICI and it appears that the knowledge of the SCM (Spatial Covariance Matrix) of the asynchronous ICI plus background noise is an important issue. Generally, it is assumed that the SCM estimated by using training symbols. However, it is difficult to measure the interference statistics for a long time and training symbol is also not appropriate for MIMO-OFDMA system such as LTE. Therefore, a noise reduction method is required to improve the estimation accuracy. Although the conventional time-domain low-pass type weighting method can be effective for noise reduction, it causes significant estimation error due to the spectral leakage in practical OFDM system. Therefore, we propose a time-domain sinc type weighing method which can not only reduce the noise effectively minimizing estimation error caused by the spectral leakage but also implement frequency-domain moving average filter easily. By using computer simulation, we show that the proposed method can provide up to 3dB SIR gain compared with the conventional method.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.727-730
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• 2002

This paper proposes the interference cancellation improvement in smart antenna system by using Applebaum array covariance matrix adjustment. This technique adds the specific adjustable multipliers with both desired signal covariance matrix and interference signal covariance matrices in order to overcome some disadvantages and improve the interference cancellation efficiency of Applebaum array. It is based on the desired and undesired signal power or desired signal-to-interference-plus-thermal noise ratio (SINR). As the result from demonstration, the proposed technique can improve and increase the interference cancellation efficiency in smart antenna better than the conventional technique.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.203-207
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• 1991

In this paper, a linear stocastic dynamic system with norm - bounded plant perpurbations and norm - bounded noise covariarice is studied. Instead of Bellman-Gronwall inequality used in previous study, Lyapunov stability theorem is used to derive stability condition. The new condition is of more compact form than the previous result.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1693-1702
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• 1999

This paper proposes the use of an adaptive Wiener filter for edge-preserving image filtering. Images are partitioned into a set of blocks of pixels which is divided into five subsets of blocks according to their edge contents and orientations. Each subset of blocks is used to define a covariance matrix, from which a Wiener filter is derived. Five covariance matrices and Wiener filters are thus obtained. An image-block classifier using the five sets of covariance matrices of the class is designed to classify each incoming block of pixels according to its edge content in the presence of noise. Experimental results are included to verify the usefulness of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.868-874
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• 2018

In this paper, we propose a new jammer suppression algorithm that uses orthogonal waveform space projection (OWSP) processing for a multiple input multiple output (MIMO) radar system exposed to a jamming signal. Generally, a conventional suppression algorithm based on adaptive beamforming (ABF) needs a covariance matrix composed of the jammer and noise only. By exploiting the orthogonality of the transmitting waveforms of MIMO, we can construct a transmitting waveform space (TWS). Then, using the OWSP processing, we can build a space orthogonal to the TWS that contains no SOI. By excluding the SOI from the received signal, even in the case that contains the SOI and jamming signal, the proposed algorithm makes it possible to evaluate the covariance matrix for ABF. We applied the proposed OWSP processing to suppressing the jamming signal in bistatic MIMO radar. We verified the performance of the proposed algorithm by comparing the SINR loss to that of the ideal covariance matrix composed of the jammer and noise only. We also derived the computational complexity of the proposed algorithm and compared the estimation of the DOD and DOA using the SOI with those using the generalized likelihood ratio test (GLRT) algorithm.

• Smart Structures and Systems
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• v.2 no.2
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• pp.127-140
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• 2006

In this study, a wavelet-based covariance-driven system identification technique is proposed for damage assessment of structures under ambient excitation. Assuming the ambient excitation to be a white-noise process, the covariance computation is shown to be able to separate the effect of random excitation from the response measurement. Wavelet transform (WT) is then used to convert the covariance response in the time domain to the WT magnitude plot in the time-scale plane. The wavelet coefficients along the curves where energy concentrated are extracted and used to estimate the modal properties of the structure. These modal property estimations lead to the calculation of the stiffness matrix when either the spectral density of the random loading or the mass matrix is given. The predicted stiffness matrix hence provides a direct assessment on the possible location and severity of damage which results in stiffness alteration. To demonstrate the proposed wavelet-based damage assessment technique, a numerical example on a 3 degree-of-freedom (DOF) system and an experimental study on a three-story building model, which are all under a broad-band excitation, are presented. Both numerical and experimental results illustrate that the proposed technique can provide an accurate assessment on the damage location. It is however noted that the assessment of damage severity is not as accurate, which might be due to the errors associated with the mode shape estimations as well as the assumption of proportional damping adopted in the formulation.

• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.206-209
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• 1993

The main purpose of this paper is to verify the effectiveness of a high resolution direction finding method, so called the‘averaged MUSIC’. This method uses a new sample array covariance matrix that consists of diagonal components obtained by taking averages of the diagonal component values of the sample covariance matrix for the MUSIC. The paper shows that the proposed method performs higher resolved direction-of-arrival estimation and better resolution probability than the MUSIC in such cases as low signal-to-noise ratio, when the number of sensors used is finite, based on the statistical analysis.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.834-838
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• 1992

Tracking performance depends on the quantity of the measurement data. In the Kalman-Bucy filter and other trackers, this dependence is well understood in terms of the measurement noise covariance matrix, which specifies the uncertainty in the value of measurement inputs. In this paper, we derived approximated error covariance matrix to evaluate the dependence of target detection probability and false alarm probability in the presence of uncertainty of measurement origin.