• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.375-382
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• 2010

In this study, we propose a robust estimator of the multivariate location parameter by means of the spatial median based on data trimming which extending trimmed mean in the univariate setup. The trimming quantity of this estimator is determined by the bootstrap method, and its covariance matrix is estimated by using the double bootstrap method. This extends the work of Jhun et al. (1993) to the multivariate case. Monte Carlo study shows that the proposed trimmed spatial median estimator yields better efficiency than a spatial median, while its covariance matrix based on double bootstrap overcomes the under-estimating problem occurred on single bootstrap method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.1
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• pp.45-51
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• 2016

In this paper, we develop an iterative maximum likelihood (ML) receiver for generalized spatial modulation systems. In the proposed ML receiver, to mitigate the deleterious effect of channel information errors on symbol detection, the instantaneous covariance matrix of effective noise is estimated, which is then used to obtain improved ML solutions. The estimated covariance matrix is updated through multiple iterations to enhance the estimation accuracy. The simulation results show that the proposed ML receiver outperforms the conventional ML detection scheme, which does not take the effect of channel information errors into account.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.963-977
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• 2011

PCA(Principal Component Analysis) is a well-studied statistical technique and an important tool for handling multivariate data. Although many algorithms exist for PCA, most of them are unsuitable for real time applications or high dimensional problems. Since it is desirable to avoid extensive matrix operations in such cases, alternative solutions are required to calculate the eigenvalues and eigenvectors of the sample covariance matrix. Erdogmus et al. (2004) proposed Recursive PCA(RPCA), which is a fast adaptive on-line solution for PCA, based on the first order perturbation theory. It facilitates the real-time implementation of PCA by recursively approximating updated eigenvalues and eigenvectors. However, the performance of the RPCA method becomes questionable as the size of newly-added data increases. In this paper, we modified the RPCA method by taking advantage of the mathematical relation of eigenvalues and eigenvectors of sample covariance matrix. We compared the performance of the proposed algorithm with that of RPCA, and found that the accuracy of the proposed method remarkably improved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.516-522
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• 2017

In this paper, we propose a maximum likelihood signal detection scheme for a generalized spatial modulation system that activates only a subset of transmit antennas among multiple antennas and transmits information through the indexes of active antennas as well as through the transmit symbols. The proposed maximum likelihood receiver extracts a set of candidate solutions based on their a posteriori probabilities to lower the computational load of the robust receiver under channel information errors. Then, the chosen candidate solutions are exploited to estimate the covariance matrix of effective noise. Simulation results show that the proposed maximum likelihood detection scheme achieves better error performance than a receiver that does not take into account the channel information errors. It is also seen that it reduces the computational complexity with the same bit error rate performance as the conventional robust maximum likelihood receiver.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6A
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• pp.450-457
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• 2012

For orthogonal frequency-division multiplexing (OFDM) systems, we propose a blind channel estimation scheme based on non-redundant precoding. In the proposed scheme, a modified covariance matrix is first obtained by dividing the covariance matrix of the received signal vector by the precoding matrix element-by-element. Then, the channel vector is estimated as an eigenvector corresponding to the largest eigenvalue of the modified covariance matrix. The eigenvector can be obtained by power method with low computational complexity instead of the complicated eigenvalue decomposition. We analytically derive a mean square error (MSE) of the proposed channel estimation scheme and show that the analysis result coincides well with the simulation result. Also, simulation results show that the proposed scheme has better MSE and bit error rate (BER) performance than conventional channel estimation schemes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1A
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• pp.84-92
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• 2004

Eigenstructure-based adaptive beamfoming has advantages of fast convergence and the insentivity to errors in the arrival angle of the desired signal. Eigen-decomposing the sample matrix to extract a basis for the Sl (signal plus interference) subspace, however, is very computationally expensive. In this paper, we present a simple subspace based beamforming which utilizes off-diagonal elements of the sample matrix to estimate the Sl subspace. The outputs of overlapped subarrays are combined to produce the final adaptive output, which improves SINR (signal-to-interference-plus-noise ratio) comapred to exploiting a single subarray. The proposed adaptive beamformer, which employs an efficient angle estimation is very roubust to errors in both the arrival angles and the number of the incident signals, while the eigenstructure-based beamforer suffers from severe performance degradation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.06a
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• pp.59-61
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• 2012

TDOA 기법은 위치추정 기법의 하나로 간단한 구조와 높은 정확도를 가지는 장점으로 인해 실내측위, 군사, 의료 분야 등에 자주 사용된다. 본 논문에서는 MPM(Matrix Pencil Method)를 이용한 고분해능 TDOA 추정 기법을 제안한다. 제안된 기법은 기존의 교차상관을 이용한 TDOA 기법에 비교하여 높은 정확도를 가지며 CW(Continuous Wave)와 같은 협대역 신호에 적용이 가능하다. 또한 잘 알려진 고분해능 기법 중 하나인 MUSIC(Multiple Signal Classification)에서 공분산 행렬을 사용하는 것과 달리 수집된 데이터를 바로 행렬로 만들어 사용하므로 복잡성이 낮은 특징이 있다. 제안된 기법의 성능을 검증하기 위해 소프트웨어 시뮬레이션 통해 SNR에 따른 오차와 연산량 측면에서 MUSIC 기법과 비교하였다.

• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.111-116
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• 2015

The purpose of this study was to evaluate mixed model of dbh-height relation containing random effect. Data were obtained from a survey site for Quercus variabilis in Gangwon region and remeasured the same site after three years. The mixed model were used to fixed effect in the dbh-height relation for Quercus variabilis, with random effect representing correlation of survey period were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -0.0291, 0.1007, respectively. The model with random effect (AIC = -215.5) has low AIC value, comparison with model with fixed effect (AIC = -154.4). It is for this reason that random effect associated with categorical data is used in the data fitting process, the model can be calibrated to fit repeated site by obtaining measurements. Therefore, the results of this study could be useful method for developing model using repeated measurement.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.591-598
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• 2021

The digital phase-locked loops(DPLL) is a circuit used for phase synchronization and has been generally used in various fields such as communication and circuit fields. State estimators are used to design digital phase-locked loops, and infinite impulse response state estimators such as the well-known Kalman filter have been used. In general, the performance of the infinite impulse response state estimator-based digital phase-locked loop is excellent, but a sudden performance degradation may occur in unexpected situations such as inaccuracy of initial value, model error, and disturbance. In this paper, we propose a minimum variance finite impulse response filter with optimal horizon for designing a new digital phase-locked loop. A numerical method is introduced to obtain the measured value interval length, which is an important parameter of the proposed finite impulse response filter, and to obtain a gain, the covariance matrix of the error is set as a cost function, and a linear matrix inequality is used to minimize it. In order to verify the superiority and robustness of the proposed digital phase-locked loop, a simulation was performed for comparison and analysis with the existing method in a situation where noise information was inaccurate.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.710-710
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• 2012

구형펄스모형(Rectangular Pulse Model)에서 반영하지 못하는 강우의 군집특성을 잘 반영하는 NSRPM(Neyman-Scott Rectangular Pulse Model) 강우생성 모형은 수자원 분야에 널리 쓰이고 있다. 일반적으로 NSRPM의 5개의 매개변수를 추정하는 최적화기법으로 DFP(Davidon-Fletcher-Powell)과 유전자알고리즘(Genetic Algorithm)을 사용하고 있다. 그러나 DFP는 주어진 초기 값에 따라 민감하며 각 반복 단계마다 헤시안행렬(Hessian Matrix)을 계산하여야 하며 추정된 전체의 해가 국지해에 수렴 할 수 있는 단점이 있다. 유전자 알고리즘을 DFP와 다르게 헤시안 행렬을 사용하지 않고 최적화를 할 수 있다는 장점이 있으나 시간이 오래 걸리는 단점이 있다. 이에 본 연구에서는 이러한 단점을 보완, 강화 하기위해서 최적화 기법으로 반복 단계마다 미분계산이 필요하지 않고 빠른 속도로 계산이 가능한 Nelder-Mead 알고리즘 이용하여 NSRPM매개변수를 추정하고 정확도를 비교하였다. 표 1은 각 기법을 이용하여 추정된 매개변수를 이용하여 생성한 강우의 통계특성과 관측된 통계특성의 상대오차를 나타낸 것이다. 괄호 안 숫자는 중첩되지 않는 누적시간을 나타낸다. 상대오차는 다음과 같다(식 1). 분석결과 Nelder-Mead 기법이 1시간의 평균, 공분산과 6시간 분산 등 전체적으로 GA, DFP보다 높은 정확도를 보였다.