• 전기학회논문지
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• 제59권11호
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• pp.2108-2113
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• 2010

The Long Term Evolution (LTE) system is based on the Orthogonal Frequency Division Multiplexing (OFDM) and relies its channel estimation on the lattice-type pilot samples in the multipath fading channel environment. The estimation of the channel frequency response (CFR) makes use of the least squares estimate (LSE) for each pilot samples, followed by an interpolation both in time- and in frequency-domain to fill up the channel estimates for subcarriers corresponding to data samples. Any interpolation scheme could be adopted for this purpose. Depending on the requirements of the target system, we may choose a simple linear interpolation or a sophisticated one. For any choice of an interpolation scheme, these is a trade-off between estimation accuracy and numerical cost. For those wireless communication systems based on the OFDM and the preamble-type pilot structure, the DFT-based channel estimation and its variants have been successfully. Yet, it may not be suitable for the lattice-type pilot structure, since the pilot samples are not sufficient to provide an accurate estimate and it is known to be sensitive to the location as well as the length of the time-domain window. In this paper, we propose a simple interpolated based on the upsampling mechanism in the multirate signal processing. The proposed method provides an excellent alternative to the DFT-based methods in terms of numerical cost and accuracy. The performance of the proposed technique is verified on a multipath environment suggested on a 3GPP LTE specification.

• 한국정보통신학회논문지
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• 제14권2호
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• pp.469-478
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• 2010

본 논문은 3차원 발 자세를 추정하기 위한 효과적 형상 기술자를 제안하였다. 처리 시간을 단축시키기 위하여 특수 제작된 3차원 발 모형을 2차원 투영하여 발 형상 데이터베이스를 구축하고, 3차원 자세 요약정보를 메타 정보로 추가한 2.5차원 영상 데이터베이스를 구성하였다. 그리고 특징 공간 크기가 작고 다른 형상 기술자에 비하여 자세 추정 성능이 뛰어난 수정된 Centroid Contour Distance를 제안하였다. 제안된 기술자의 성능을 분석하기 위하여, 검색 정확도와 시공간 복잡도를 계산하고 기존의 방식들과 비교하였다. 실험 결과를 통하여 제안된 기술자는 특징 추출 시간과 자세 추정 정확도면에서 기존의 방식들보다 효과적인 것으로 나타났다.

• 대한전기학회논문지:전력기술부문A
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• 제51권1호
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• pp.29-36
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• 2002

It is required that the current state of system should be Precisely monitored for efficient and safe operation of the subway power system and it is an important Problem to secure the high quality data for state estimation. The current state of subway power system is estimated by using data transmitted to control center from every measuring instrument. The high accuracy and trust can be maintained if the measured data have a high quality. But it is difficult to estimate the accurate state of system because of the noises in transmitted data and the inaccuracy of measuring instruments. So the object is to reduce the difference between the real values and the measured values in order to improve considerably the inaccuracy due to Instrumental errors and transmission noises using the state estimation method. In this paper, we proposes a new state estimation to estimate the accurate state of the subway power system from the measured values of a Sang-In station in Daegu subway and consider the possibility of application to the real subway power system. on the basis of that. The simulation results show to make sure of the possibility to apply to the real system usefully.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2010년도 한국우주과학회보 제19권1호
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• pp.27.5-28
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• 2010

The unexpected observation condition or insufficient measurement modeling can lead to uncertain measurement errors. The uncertain measurement error of orbit determination problem typically consists of noise, bias and drift. It must be removed by using a proper estimation process for better orbit accuracy. The estimation of noise and drift is not easy because of their random or unpredictable variation. On the other hand, bias is a constant difference between the mean of the measured values and the true value, so it can be simply removed. In this study, precision orbit determination with SLR observations considering range bias estimation is presented. The Yonsei Laser-ranging Precision Orbit Determination System (YLPODS) and SLR NP (Normal Point) observations of CHAMP satellite are used for this work. The SLR residual test is performed to estimate the range bias of each arc. The result shows that we can get better orbit accuracy through range bias estimation.

• 한국측량학회지
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• 제23권2호
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• pp.137-145
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• 2005

이 논문에서는 선형 푸시브룸 방식으로 촬영한 위성영상에 대한 다양한 센서모델들의 정확도를 비교분석하고자 한다. 특히 이 논문에서는 센서모델의 정확도를 번들조정의 정확도와 외부표정요소추정의 정확도로 분리하여 고찰하려고 한다. 번들조정 정확도는 수립된 센서모델이 얼마나 기준점에 잘 부합하는 가를 알려주는 척도로 이제까지 대부분의 센서모델 정확도 분석에 사용되어온 기준이다. 이에 반하여 외부표정요소추정의 정확도는 센서모델이 얼마나 정확하게 촬영당시의 위성의 궤도 및 자세를 예측할 수 있는 지의 척도로서 매우 중요한 요소임에도 불구하고 기존의 연구에서 간과해온 부분이다. 이 논문에서는 여러 센서모델 중에서 사진측량분야에서 주로 사용하는 변형된 공선방정식기반 모델과 위성지상국 또는 자세제어분야에서 주로 사용하는 궤도좌표계 및 자세각에 기반한 모델의 정확도를 비교분석하고자 한다. 실험은 다목적실용위성 1호 EOC 영상과 GPS 수신기에서 취득한 기준점을 사용하였다. 실험결과 번들조정 정확도는 두 모델이 큰 차이를 보이지 않는 것으로 나타났으나 외부표정요소추정 정확도는 궤도좌표계 및 자세각에 기반한 모델이 더 나은 성능을 보였다.

• 대한전기학회논문지
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• 제39권4호
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• pp.380-385
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• 1990

This Paper presents a general algorithm for the parameter estimation of an antoregressive moving average process observed in additive white noise. The algorithm is based on the Gauss-Newton recursive prediction error method. For the parameter estimation, the output measurement is modelled as an innovation process using the spectral factorization, so that noise free RPE ARMA estimation can be used. Using apriori known properties leads to algorithm with smaller computation and better accuracy be the parsimony principle. Computer simulation examples show the effectiveness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.141.4-141
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• 2001

In this paper the Local Weighted Regression LWR technique is applied to the estimation of aircrcraft parameters. The method consists In improving the Local Weighted Regression LWR technique by adding a data Retention-and-Deletion RD strategy. The improvement comes with reduced computational effort since the two techniques can share their main computational procedures. The purpose of the study was to establish if the proposed algorithm could provide fast and reliable real-time estimations, with accuracy comparable to other well-known off-line identification schemes. The algorithm was tested using specific parameter estimation maneuvers and flight data of the NASA F/A-18 HARV. The results were compared with both the estimation obtained from ...

• 한국화재소방학회논문지
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• 제10권2호
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• pp.13-19
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• 1996

An estimation methodology, based on statistics and numerical method, has been developed for estimating the upper explosive limits(UEL) of paraffinic and olefinic hydrocarbon compounds. With proposed method, the UEL has been calculated for 24 paraffinic and 10 olefinic hydrocarbon compounds. The estimated the UEL agree with the experimental values within a few percent. A comparisond with four other methods avaiable in the literature are also presented. It is hoped eventually that this method will permit estimation of the UEL with improved accuracy and broader application for other compounds.

• 한국통신학회논문지
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• 제24권9B호
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• pp.1697-1703
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• 1999

본 논문에서는 반화소 위치에서의 평균 절대 오차 (mean absolute difference; MAD)의 최소 범위를 이용한 고속 반화소 단위의 블록 정합 알고리듬을 제안한다. 제안한 방법에서는 반화소 단위의 MAD의 최소 범위를 화소 단위의 움직임 추정으로 얻어진 MAD들을 이용하여 구한 후, 반화소 단위의 MAD의 최소 범위가 화소 단위의 움직임 추정으로 구한 기준 MAD보다 작은 탐색점에 대해서만 블록 정합을 행하였다. 제안한 방법의 성능을 평가하기 위한 컴퓨터 모의 실험결과로부터 제안한 방법이 기존의 방법과 동일한 성능을 유지하면서도 많은 계산량을 줄일 수 있음을 확인할 수 있었다.

• 대한산업공학회지
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• 제39권2호
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• pp.109-118
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• 2013

We propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved performance of the MLPAR in terms of prediction accuracy.