Earticle

위성 기반 측위 시스템은 위치 기반 기술의 핵심 기술로서, CDMA 시스템을 사용한다. CDMA 시스템에서 원활한 위치 측위를 하기 위해서는 확산 부호의 정확한 동기가 이루어져야 한다. 본 논문은 확산 부호의 동기 기법 가운데 부호 추적 기법에 초점을 맞춘다. 특히 다중 경로 환경에서 기존의 부호 추적 기법인 DLL 방법은 적합하지 않다. 본 논문은 수신 신호에서 다중 경로 신호를 축출하여 이를 부호 추적 기법에 사용하는 방법을 제안한다. Spirent simulator를 이용하여 제안 기법을 검증하고 성능 평가를 보인다.

The navigation system like GPS which is core technology is based on Code Division Multiple Access(CDMA) techniques. To receive satellite signal smoothly in CDMA, received signals have to synchronize with spread code. In this paper, we focus on the code tracking methods among synchronization techniques. The conventional delay lock loop(DLL) is unsuitable for multi-path channel. We will introduce how it overcomes distortion by multi-path. We will propose method that separates out multi-path signals and tracks the each path signals. And we will confirm performance of proposed method using Spirent simulator.

Maximum likelihood is a criterion often used to derive localization algorithms. In particular, in this paper we focus on a distance-based algorithm for the localization of nodes in static wireless networks. Assuming that Ultra Wide Band (UWB) signals are used for inter-node communications, we investigate the ill-conditioning of the Two-Stage Maximum-Likelihood (TSML) Time of Arrival (ToA) localization algorithm as the Anchor Nodes (ANs) positions change. We analytically derive novel lower and upper bounds for the localization error and we evaluate them in some localization scenarios as functions of the ANs’ positions. We show that particular ANs’ configurations intrinsically lead to ill-conditioning of the localization problem, making the TSML-ToA inapplicable. For comparison purposes, we also show, through some examples, that a Particle Swarm Optimization (PSO)-based algorithm guarantees accurate positioning also when the localization problem embedded in the TSML-ToA algorithm is ill-conditioned.

정확한 인식률을 보이고 있는 상업적인 음성인식 시스템은 화자종속 고립데이터로부터 학습 모델을 사용한다. 그러 나 잡음 환경에서 데이터양에 따라 음성인식의 성능이 저하되는 문제점이 있다. 본 논문에서는 가우시안 분포에서 Maximum Log Likelihood를 이용한 벡터 양자화 기반 음성 인식 성능 향상을 제안한다. 제안하는 방법은 음성에 대한 특징 을 가지고 벡터 양자화와 Maximum Log Likelihood 음성 특징 추출 방법을 이용하여 유사 음성에 대한 음성 인식의 정확성 을 높이는 최적 학습 모델 구성 방법이다. 이를 위해 HMM을 기반으로 음성 특징을 추출하는 방법을 사용한다. 제안하는 방법을 사용하여 기존 시스템에서 생성되어 사용되는 음성 모델에 대한 부정확한 음성 모델에 대한 정확성을 향상시킬 수 있으므로 음성 인식에 강인한 모델을 구성할 수 있다. 제안하는 방법은 음성 인식 시스템에서 향상된 인식의 정확도를 보인다.

Commercialized speech recognition systems that have an accuracy recognition rates are used a learning model from a type of speaker dependent isolated data. However, it has a problem that shows a decrease in the speech recognition performance according to the quantity of data in noise environments. In this paper, we proposed the vector quantization based speech recognition performance improvement using maximum log likelihood in Gaussian distribution. The proposed method is the best learning model configuration method for increasing the accuracy of speech recognition for similar speech using the vector quantization and Maximum Log Likelihood with speech characteristic extraction method. It is used a method of extracting a speech feature based on the hidden markov model. It can improve the accuracy of inaccurate speech model for speech models been produced at the existing system with the use of the proposed system may constitute a robust model for speech recognition. The proposed method shows the improved recognition accuracy in a speech recognition system.

A sample with special missing pattern called a monotone sample or a monotone missing data pattern occurs in many applications. The statistical methods for analyzing a monotone sample have been developed by several analysts. The purpose of this paper is to introduce the maximum likelihood estimation of parameters when there is a multivariate normal sample with 2-step monotone pattern, and to present the maximum likelihood estimates of parameters in closed forms. These estimates can be obtained by maximizing the overall likelihood function which is expressed in terms of the marginal and conditional likelihood functions with respect to parameters. The maximum likelihood estimates presented are expected to he used to develop statistical methods for analyzing a multivariate normal sample with 2-step monotone pattern in applications.

Background/Objectives: This study was designed to compare the Varimax analysis of Principal Component Analysis(PCA) with the Exploratory Factor Analysis(EFA) method using Maximum Likelihood Estimation(MLE) and oblique rotation(Direct Oblimin) for the multicultural experience of university students. multicultural experience variables of college students were analyzed as two factors through Maximum Likelihood Estimation(MLE). Methods/Statistical analysis Q1, Q2, Q3 are multicultural direct experiences, and Q4, Q5, Q6, Q7 are multicultural indirect experiences. Findings: As a result of this study, it is suggested to use Maximum Likelihood Estimation(MLE) and oblique rotation(Direct Oblimin). As a prerequisite for applying the Maximum Likelihood Estimation(MLE), it is thought that Improvements/Applications it is necessary to increase the accuracy of the response rate by using the face-toface interview method when conducting a survey.

통계 모형을 이용한 데이터 분석에서 적절한 결측치 처리가 최종적인 통계적 결론 도출에 결정적인 영향을 미칠 수 있다는 것은 이미 잘 알려져 있다. 따라서 현재까지 다양한 결측치 처리 기법이 제안되어왔고, 그 중 최근 활발히 연구되고 있는 기법으로서 완전정보최대우도(full information maximum likelihood), 다중대체(multiple imputation), 그리고 베이지안(Bayesian) 접근법이 있다. 이 중 완전정보최대우도법은 사회과학 통계에서 가장 많이 사용되는 방법으로서 많은 심리학 연구자들에게도 이미 알려져 있는 방법이나, 다른 두 방법, 즉 다중대체 및 베이지안 접근법은 아직은 많은 심리학 연구자들에게 생소한 방법이다. 따라서 본 논문에서는 이 세 가지 방법을 공분산 구조모형의 맥락에서 소개하고 주요 특징을 비교함으로써 심리학 연구자들의 결측치 처리에 관한 이해를 돕고자 하였다. 공분산구조모형의 적용 과정은 다른 통계모형과 달리 자료와 모형간의 적합도 지수를 계산하고 이를 바탕으로 모형의 적절성을 판단하는 과정을 포함한다. 최대우도법에서는 카이자승 통계량을 기준으로 다양한 적합도 지수가 파생되어 제안되었으며, 다중대체법은 D2와 D3 통계량이, 그리고 베이지안 접근법에서는 사후예측모형검증(posterior predictive model checking) 기법이 사용된다. 최대우도법의 카이자승 통계량은 다양한 맥락에서 독립적으로 연구된 결과가 있으나, 다중대체법 및 베이지안 접근법에서 제안된 방법은 공분산 구조모형의 맥락에서 그 성능이 평가된 적이 없으며 서로 비교된 적도 없다. 따라서, 본 논문에서는 동일한 구조방정식모형의 자료-모형간 적합성 판단에 있어서 세 가지 다른 결측치 처리 방법이 어떤 영향을 미치는지 비교 분석하였다. 구체적으로, 본 논문에서는 모의실험(simulation) 기법을 사용하여, 모집단에서 데이터가 종단적 측정 불변성을 지지하지 않는 경우(longitudinal measurement non-invariance)를 가정하고, 옳은 모형을 사용한 경우의 제 1 종 오류율과 부분측정불변성(partial measurement invariance)을 가정한 모형을 적용한 경우의 검정력을 최대우도, 다중대체, 그리고 베이지안 접근법 별로 추정/비교하였다. 본 연구의 결과는 결측치가 존재하는 데이터를 공분산 구조모형을 이용하여 분석하고자 하는 연구자들에게 결측치 처리 기법의 특성을 이해하고 최종 모형의 적합성을 판단하는데 적절한 지침을 제공할 것으로 기대된다.

In practical applications of any statistical modeling, including structural equation modeling(SEM), virtually every data set contains missing values. It is a well known fact that improper handling of missing data can exert harmful impact on subsequent statistical inferences in a variety of ways to varying degrees. In the context of SEM, the full information maximum likelihood(FIML) has been arguably the most popular method for addressing missing data. Despite of being yet less widely known to majority of applied researchers as flexible alternatives to FIML, multiple imputation (MI) procedures and Bayesian approaches have recently begun to emerge as viable solutions among many applied researchers. An important objective of this article is to introduce these methods to applied researchers in an accessible manner using SEM as the context. Structural equation modeling actually involves the process of proposing, estimating, and evaluating the researcher’s hypothesis that is believed to be underlying and purported in generating the observed data. Therefore, it is essential to evaluate the overall goodness-of-fit of the posited model in any given application. FIML, MI and Bayesian approaches, respectively, yield the chi-square, , , and the posterior predictive modeling checking (PPMC) p-value as statistical tools for the assessment of data-model fit. Another important objective of this article is to study performance of these model evaluation tools in the context of SEM. Further, relative performance of these data-model fit assessment tools is to be evaluated with respect to their Type I error rates and power. The performance of these assessment tools, except the chi-square statistics, has never been evaluated nor been compared within the context of SEM. The initial results provided in the present article is believed to not only enhance the knowledge base regarding the characteristics of these assessment tools under missing data, but also provide an initial guideline for the proper use of these assessment tools in the real-world data analysis especially in the application of SEM with missing data.

본 논문에서는 Wireless PAN을 위한 MIMO-OFDM시스템에서 근거리 데이터 전송과 더불어 주변장치간 원활한 통신을 위해 채널 sounding 기법을 제안한다. 제안된 기법은 전력 확산 지연을 통해서 임펄스 응답의 지속시간을 파악하며, 감쇠 특성을 갖는 전력 지연 프로 파일(power delay profile) 모델링 하였고, ML(maximum likelihood) 기법으로 채널 응답 계수를 얻었다. Channel sounding 기법을 통해 측정한 크기, 위상, 지연의 변수 값으로 송신단과 수신단간의 채널 전파 특성이 측정 된다. 이 측정된 채널 정보들이 수신단에서 채널 상태를 파악하며, 수신단에서 이 채널 정보를 근거로 통신 링크 성능 향상과 신뢰성을 증대시킬 수 있는 기법을 제안하였다.

In this paper, we propose the channel sounding scheme which is made for ideal communication between some application as well as the short distance of high speed data transmission in MIMO-OFDM system for Wireless PAN. This method is able to perceive the duration of the impulse response through the delaying of power delay profile, modeled a power delay profile which has an attenuate characteristic, and obtained the coefficient of channel response by ML (maximum likelihood). Through the amplitudes, phases and delays associated with each multipath component which were acquired from this channel sounding scheme, we can describe the wave propagation characteristics of channels between the transmitter and receiver so that the receiver could enhance not only the reliability but also the ability of communication link.

In this paper, we investigate the state estimation problem of nonlinear systems under the condition that the prior statistical characteristic of noise is unknown. An adaptive unscented Kalman filter (UKF) is proposed. In this algorithm, the maximum likelihood principle is applied to establish the log likelihood function with the unknown noise statistical characteristics. Then, the noise property estimation problem is transformed into the maximization of the mean of the log likelihood function, which can be achieved by using the expectation maximization algorithm. Finally, a suboptimal adaptive UKF can be obtained. Simulations show that the proposed adaptive UKF algorithm can deal with the problem of filtering accuracy declination of the traditional UKF when the prior noise statistical characteristic is unknown. The proposed algorithm can estimate the statistical parameters online.

In this paper we propose a hybrid maximum likelihood decoding (MLD) for linear block codes. For the reliable data transmission over noisy channels, convolutional and block codes are widely used in most digital communication systems. Much more efficient algorithms have been found for using channel measurement information in the decoding of convolutional codes than in the decoding of block codes. Word correlation method can be utilized to use channel measurement information in the decoding of block codes. However as the number of code words becomes larger, the decoding complexity increases dramatically to the power of the number of information bits. The hybrid maximum likelihood decoding can solve the problem of the hardware complexity as well as the computational time. Simulation result for Reed Muller code is presented to demonstrate the effectiveness of the algorithm.

In this paper, an interaural time difference (ITD) estimation method is proposed for binaural speech separation in reverberant environments. First, the auditory signals are represented in the time-frequency (T-F) domain, and the ITD for each T-F bin is then estimated using generalized cross-correlation (GCC) with a maximum likelihood (ML) weighting function. In particular, the ML weighting function is designed to reduce the reverberation effect. Then, a mask is estimated by comparing the estimated ITD with the ITD corresponding to the location of the pre-defined target speech source. Finally, the target speech is separated by applying the mask to the auditory signals. It is shown that the proposed ITD estimation method outperforms a conventional cross-correlation-based ITD estimation method under reverberant conditions in terms of the signal-to-noise ratio (SNR) and signal-to-distortion ratio (SDR) of the separated speech signals.

공간상에서 원하는 목표물의 도래 방향 추정은 수신 안테나에 입사하는 신호의 입사 방향을 찾는 것이다. 본 논문에서는 최대 우도 추정 방법을 이용하여 원하는 목표물의 도래 방향을 추정하였다. 도래 방향 추정방법은 최대 우도 방법에서 수신 신호 한계점 이상의 신호에 특이 값 분해를 적용하여 최대 우도 추정의 첨예도를 계산하여 원하는 목표물을 추정하였다. 모의실험을 통하여 본 연구에서 제안된 방법의 성능을 기존 방법과 비교분석하였다. 목표물 도래방향 추정에서 본 연구에서 제안한 방법이 고유치 전개를 하지 않기 때문에 처리시간 단축에서 효과적이고 원하는 목표물의 방향을 정확히 추정하였다. 본 연구에서 제안한 방법이 목표물 추정에서 기존 방법보다 우수함을 나타내었다.

In spatial, desired target direction of arrival estimation is to find a incidental signal direction on receive antennas. In this paper, we were an estimation a desired target direction of arrival using maximum likelihood method. Direction of arrival estimation method estimated a desired target calculating the maximum likelihood sensitivity using singular value decomposition above threshold signals among receive signals in maximum likelihood method. Through simulation, we were analysis a performance to compare existing method and proposal method. In direction of arrival estimation, proposed method is effectivity to decrease processing time because it is not doing an eigen decomposition in direction of arrival estimation, and desired target correctly estimated. We showed that proposal method improve more target estimation than general method.

본 논문에서는 최대 우도 (ML: maximum likelihood) 등화기를 적용한 협력통신 시스템의 성능을 분석하고 모의실험 하였다. 무선통신 시스템에서 통신 커버리지를 확장하기 위해서 다수의 중계기를 이용한 협력통신 개념이 적용된다. 중계기와 사용자 단말기는 모두 이동할 수 있는 것으로 가정한다. 따라서 두 개 이상의 중계기의 커버리지가 서로 겹칠 수 있다. 만약 원하는 신호와 간섭 신호가 동일 채널을 통해 전송되고 커버리지가 중복된 부분에 하나의 수신 단말이 존재한다면, 간섭신호로 인해 수신기의 성능은 열화된다. 따라서 본 논문에서는 통신 시스템의 성능을 향상시키고 간섭 신호의 영향을 제거하기 위해 최대 우도 등화기를 적용하였다. 협력통신 시스템의 성능은 비트 오류율 측면에서 평가되었다. 실험 결과로부터 최대 우도 수신기는 복잡도가 높지만 간섭 제거 성능이 우수한 것을 확인하였다.

In this paper, we analyze and simulate the performance of a cooperative communication system adopting a maximum likelihood (ML) equalizer. In wireless communication systems, cooperative communication schemes employing several relays can be applied for extending the communication coverage. It is assumed that both relays and user terminals can move. Therefore, coverages of two or more relays can overlap each other. If wanted and interfering signals are transmitted through the same channel and there are one terminal in the overlapped region, its performance is degraded due to interference. Hence, we use a ML equalizer for rejecting the effect of interfering signal and enhancing the communication system performance. The cooperative system performance is evaluated in terms of bit error probability. From the simulation results, it is demonstrated that the ML receiver shows good interference cancellation performance although its complexity is high.

We consider the problem of designing independently operating local quantizers at nodes in distributed estimation systems, where many spatially distributed sensor nodes measure a parameter of interest, quantize these measurements, and send the quantized data to a fusion node, which conducts the parameter estimation. Motivated by the discussion that the estimation accuracy can be improved by using the quantized data with a high probability of occurrence, we propose an iterative algorithm with a simple design rule that produces quantizers by searching boundary values with an increased likelihood. We prove that this design rule generates a considerably reduced interval for finding the next boundary values, yielding a low design complexity. We demonstrate through extensive simulations that the proposed algorithm achieves a significant performance gain with respect to traditional quantizer designs. A comparison with the recently published novel algorithms further illustrates the benefit of the proposed technique in terms of performance and design complexity.

This paper addresses the issue of time-difference-of-arrival (TDOA) estimation for complex noncircular signals. First, under the wide-sense stationary assumption, we derive the maximum likelihood (ML) estimator and the Cramer-Rao lower bound for Gaussian noncircular signals in Gaussian circular noise. The ML estimator uses the second-order statistics information of a noncircular signal more comprehensively when compared with the cross-correlation (CC) and the conjugate CC estimators. Further, we present a scheme to modify the traditional signal-selective TDOA methods for noncircular signals on the basis of the cyclostationarity of man-made signals. This scheme simultaneously exploits the information contained in both the cyclic cross-correlation (CCC) and the conjugate CCC of a noncircular signal.

For the acoustic models of embedded speech recognition systems, hidden Markov models (HMMs) are usually quantized and the original full space distributions are represented by combinations of a few quantized distribution prototypes. We propose a maximum likelihood objective function to train the quantized distribution prototypes. The experimental results show that the new training algorithm and the link structure adaptation scheme for the quantized HMMs reduce the word recognition error rate by 20.0%.

Poisson generalized linear mixed models(GLMMs) have been widely used for the analysis of clustered or correlated count data. For the inference marginal likelihood, which is obtained by integrating out random effects is often used. It gives maximum likelihood(ML) estimator, but the integration is usually intractable. In this paper, we propose how to obtain the ML estimator via Laplace approximation based on hierarchical-likelihood (h-likelihood) approach under the Poisson GLMMs. In particular, the h-likelihood avoids the integration itself and gives a statistically efficient procedure for various random-effect models including GLMMs. The proposed method is illustrated using two practical examples and simulation studies.

This paper considers the maximum likelihood (ML) detection of 16-ary differential amplitude and phase shift keying (DAPSK) in Rayleigh fading channels. Based on the conditional likelihood function, two new receiver structures, namely ML symbol-by-symbol receiver and ML sequence receiver, are proposed. For the symbol-by-symbol detection, the conventional DAPSK detector is shown to be sub-optimum due to the complete separation in the phase and amplitude detection, but it results in very close performance to the ML detector provided that its circular amplitude decision thresholds are optimized. For the sequence detection, a simple Viterbi algorithm with only two states are adopted to provide an SNR gain around 1 dB on the amplitude bit detection compared with the conventional detector.

This paper deals with the problem of estimating the means in two inverse Gaussian populations with equal but unknown coefficient of variation. The maximum likelihood estimators are derived by solving a cubic equation and their asymptotic variances are presented for comparative purpose. Monte-Carlo simulation is conducted to investigate the efficiency of the estimators relative to the sample means over a wide range of values for the sample size and the coefficient of variation. The effect on this efficiency under the departure from the assumption of common coefficient of variation is also studied.