• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.11
• /
• pp.2108-2113
• /
• 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.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.2
• /
• pp.469-478
• /
• 2010

This paper proposes the effective shape descriptor for 3D foot pose estimation. To reduce processing time, silhouette-based foot image database is built and meta information which involves the 3D pose of the foot is appended to the database. And we proposed a modified Centroid Contour Distance whose size of the feature space is small and performance of pose estimation is better than the others. In order to analyze performance of the descriptor, we evaluate time and spatial complexity with retrieval accuracy, and then compare with the previous methods. Experimental results show that the proposed descriptor is more effective than the previous methods on feature extraction time and pose estimation accuracy.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.51 no.1
• /
• pp.29-36
• /
• 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.

• Bulletin of the Korean Space Science Society
• /
• 2010.04a
• /
• pp.27.5-28
• /
• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.23 no.2
• /
• pp.137-145
• /
• 2005

In this paper, we investigate the accuracy of various sensor models developed for linear pushbroom satellite images. We define the accuracy of a sensor model in two aspects: the accuracy of bundle adjustments and the accuracy of estimating exterior orientation parameters. The first accuracy has been analyzed and reported frequently whereas the second accuracy has somewhat been neglected. We argue that the second accuracy is as important as the first one. The second accuracy describes a model's ability to predict satellite orbit and attitude, which has many direct and indirect applications. Analysis was carried out on the traditional collinearity-based sensor models and orbit-based sensor models. Collinearity-based models were originally developed for aerial photos and modified for linear pushbroom-type satellite images. Orbit-based models have been used within satellite communities for satellite control and orbit determination. Models were tested with two Kompsat-1 EOC scenes and GPS-driven control points. Test results showed that orbit-based models produced better estimation of exterior orientation parameters while maintained comparable accuracy on bundle adjustments.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.4
• /
• pp.380-385
• /
• 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
• /
• 2001.10a
• /
• pp.141.4-141
• /
• 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 ...

• Fire Science and Engineering
• /
• v.10 no.2
• /
• pp.13-19
• /
• 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.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.9B
• /
• pp.1697-1703
• /
• 1999

In this paper, we propose the fast block matching algorithm with half pel accuracy using the lower bound of mean absolute difference (MAD) at search point of half pel accuracy motion estimation. The proposed method uses the lower bound of MAD at search point of half pel accuracy which calculated from MAD's at search points of integer pel accuracy. We can reduce the computational complexity by executing the block matching operation only at the necessary search point. The points are selected when the lower bound of MAD at that point is smaller than reference MAD of integer pel motion estimation. Experimental results show that the proposed method can reduce the computational complexity considerably and keeping the same performance with conventional method.

• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.2
• /
• pp.109-118
• /
• 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.