• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.11
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• pp.1868-1878
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• 2016

This paper proposes a novel velocity estimator for long-term underwater navigation of autonomous underwater vehicles(AUVs). Provided that an external position fix is not given, a viable goal in designing a underwater navigation algorithm is to reduce the divergence rate of position error only using the sporadic velocity information obtained from Doppler velocity log(DVL). For such case, the performance of underwater navigation eventually depends on accuracy and reliability of external velocity information. This motivates us to devise a velocity estimator which can drastically enhance the navigation performance even when the DVL measurement is unavailable. Incorporating the Gertler-Hagen hydrodynamics model of an AUV with the measurement models of velocity and depth sensors, the velocity estimator design problem is resolved using the extended Kalman filter. Different from the existing methods in which an AUV simulator is regarded as a virtual sensor, our approach is less sensitive to the model uncertainty often encountered in practice. This is because our velocity filter estimates the simulator errors with sensor aids and furthermore compensates these errors based on the indirect feedforward manner. Through the simulations for typical AUV navigation scenarios, the effectiveness of the proposed scheme is demonstrated.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.457-476
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• 2000

In this study, we have proposed a sampling method and an estimation method for efficiently estimating the mean of a population which has a linear trend. These methods involve drawing a sample by the so-called "centered balanced systematic sampling", which is an extension of systematic sampling, and then estimating the population mean with an adjusted estimator, not with the sample mean itself. We used the concept of interpolation in determining the adjusted estimator.\Ve compared the efficiency of the proposed estimator with those of the estimators from existing methods, under the expected mean square error criterion based on the infinite superpopulation model introduced by Cochran(1946). The proposed method is for use in the case when the sample size n(2 5) is an odd number and k(the reciprocal of the sampling fraction) is an even number. A good result was also obtained in an example using computer simulation. simulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.81-86
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• 2020

Ambient backscatter communication has a drawback in which the transmission power is limited because the data is transmitted using the ambient RF signal. In order to improve transmission efficiency between transceiver, a channel estimator capable of estimating channel state at a receiver is needed. In this paper, we consider the K-means algorithm to improve the performance of the channel estimator based on EM algorithm. The simulation uses MSE as a performance parameter to verify the performance of the proposed channel estimator. The initial value setting through K-means shows improved performance compared to the channel estimation method using the general EM algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4967-4986
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• 2015

Multi-hop relaying communications have great potentials in improving transmission performance by deploying relay nodes. The benefit is critically dependent on the accuracy of the channel state information (CSI) of all the transmitting links. However, the CSI has to be estimated. In this paper, we investigate the channel estimation problem in one-way multi-hop MIMO amplify-and-forward (AF) relay system, where both the two-hop and three-hop communication link exist. Traditional point-to-point MIMO channel estimation methods will result in error propagation in estimating relay links, and separately tackling the channel estimation issue of each link will lose the gain as part of channel matrices involved in multiple communication links. In order to exploit all the available gains, we develop a novel channel estimation model by structuring different communication links using the PARAFAC and PARATUCK2 tensor analysis. Furthermore, a two-stage fitting algorithm is derived to estimate all the channel matrices involved in the communication process. In particular, essential uniqueness is further discussed. Simulation results demonstrate the advantage and effectiveness of the proposed channel estimator.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1277-1282
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• 2016

In the next generation wireless communication systems, an energy harvesting from radio frequency signals is considered as a method to solve the lack of power supply problem for sensors. In this paper, we try to propose an efficient algorithm for simultaneous wireless information and power transfer in energy harvesting networks with channel estimation error. At first, we find an optimal channel training interval using one-dimensional exhaustive search, and estimate a channel using MMSE channel estimator. Based on the estimated channel, we propose a power allocation and splitting algorithm for maximizing the data rate while guaranteeing the minimum required harvested energy constraint, The simulation results confirm that the proposed algorithm has an insignificant performance degradation less than 10%, compared with the optimal scheme which assumes a perfect channel estimation, but it can improve the data rate by more than 20%, compared to the conventional scheme.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.493-505
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• 2017

Maximum likelihood estimation (MLE) of the generalized extreme value distribution (GEVD) is known to sometimes over-estimate the positive value of the shape parameter for the small sample size. The maximum penalized likelihood estimation (MPLE) with Beta penalty function was proposed by some researchers to overcome this problem. But the determination of the hyperparameters (HP) in Beta penalty function is still an issue. This paper presents some data adaptive methods to select the HP of Beta penalty function in the MPLE framework. The idea is to let the data tell us what HP to use. For given data, the optimal HP is obtained from the minimum distance between the MLE and MPLE. A bootstrap-based method is also proposed. These methods are compared with existing approaches. The performance evaluation experiments for GEVD by Monte Carlo simulation show that the proposed methods work well for bias and mean squared error. The methods are applied to Blackstone river data and Korean heavy rainfall data to show better performance over MLE, the method of L-moments estimator, and existing MPLEs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7A
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• pp.623-631
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• 2010

In this paper, various of pilot-assisted channel estimation techniques for 3GPP LTE downlink are tested under multipath Rayleigh fading channel. At first, the conventional channel estimation techniques are applied with linear zero-forcing (ZF) equalizer, such as one dimensional least square (1-D LS) linear interpolation, two dimensional (2-D) wiener filter, the time and frequency dimension separate wiener filter and maximum likelihood estimator (MLE). Considering the practical implementation, we proposed two channel estimation techniques by combining time-dimension wiener filter and MLE in two manners, which showed a good tradeoff between system performance and complexity when comparing with conventional techniques. The nonlinear decision feedback equalizer (DFE) which can show a better performance than linear ZF equalizer is also implemented for mitigating inter-carrier interference (ICI) in our system. The complexity of these algorithms are calculated in terms of the number of complex multiplications (CMs) and the performances are evaluated by showing the bit error rate (BER).

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.7
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• pp.9-17
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• 2000

Determining the position and velocity of mobiles is an important issue for efficient handoff and channel allocation in microcell structure. Our early work proposes a technique for estimating the mobile location in the microcellular architecture. This process is based on the three step position estimation which determines the mobile position by gradually reducing the area of the mobile position. Using three step method, the estimator first estimates the locating sector in the sector estimation step, then estimates the locating zone in the zone estimation step, and then finally estimates the locating block in the block estimate step. But this scheme is prone to errors when the mobile is located in the boundary of sectors or tracks. In this paper we propose the enhanced scheme to reduce the estimation error.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2A
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• pp.43-50
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• 2005

In this paper, we present an improved channel estimation method for orthogonal frequency division multipexing systems using pilot symbol assisted modulation(PSAM). Conventional linear minimum mean square error(LMMSE) channel estimation method uses only pilot symbols for channel estimation. So, as the fading channel varies rapidly, the system performance is degraded. The basic idea of the proposed scheme is that we firstly estimate channel coefficients at the middle point between two pilot symbols and then compute the channel attenuation by using LMMSE method. Superior performance achieved with the proposed method is illustrated by simulation experiments with the Doppler frequency of 36Hz and 185Hz in comparison with conventional LMMSE channel estimator.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.291-301
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• 2017

It is well known in a small sample that the maximum likelihood (ML) approach for variance components in the general linear model yields estimates that are biased downward. The ML estimate of residual variance tends to be downwardly biased. The underestimation of residual variance, which has implications for the estimation of marginal effects and asymptotic standard error of estimates, seems to be more serious in some limited dependent variable models, as shown by some researchers. An alternative frequentist's approach may be restricted or residual maximum likelihood (REML), which accounts for the loss in degrees of freedom and gives an unbiased estimate of residual variance. In this situation, the REML estimator is derived in a censored regression model. A small sample the REML is shown to provide proper inference on regression coefficients.