• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.7
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• pp.1534-1542
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• 1997

In this paper, we present a performance improvement method for the direction-of-arrival (DOA) estimation algorithm of the narrowband signals incident on a uniform circular array. It is very important to estimate the covariance matrix effectively because the performance of DOA algorithm mainly depends on the exactness of the sampel coveriance matrix which is computed from the received samples of signals. In case of uniform circular array with the even number sensors, the structure of the arrray has a useful geometrical property. Therefore we present the method which can estimate covariance matrix more effectively using this property. The simulation results are shown to demonstrate the superior perfodrmance obtained by the proposed covariance matrix estimation method relative to that of the conventional estimation method.

• Korean Journal of Agricultural and Forest Meteorology
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• v.1 no.1
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• pp.60-71
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• 1999

The micrometeorological tower flux network is the cornerstone of the global terrestrial vegetation monitoring. The eddy covariance technique used for tower fluxes is derived from the conservation of mass and is most applicable for steady-state conditions over flat, extended, and uniform vegetation. This technique allows us to obtain surface fluxes of energy budget components, greenhouse and trace gases, and other pollutants. The quality-controlled flux data are invaluable to validate various models with temporal scales ranging from minutes to years and spatial scales ranging from a few meters to hundreds of kilometers. In this paper, we review the theoretical background of this important eddy covariance technique, examine the measurement criteria and corrections, and finally suggest some measurement strategies that may facilitate coordinated flux measurements among different disciplines and provide a strong infrastructure for the global flux network.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.457-462
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• 2005

An empirical way of a covariance matrix which expresses the odometry uncertainty of mobile robots is proposed. This method utilizes PC-method which removes systematic errors of odometry. Once the systematic errors are removed, the odometry error can be modeled using the Gaussian probability distribution, and the parameters of the distribution can be represented by the covariance matrix. Experimental results show that the method yields $5{\%}$ and $2.3{\%}$ offset for the synchro and differential drive robots.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.575-585
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• 2016

Longitudinal studies repeatedly measure outcomes over time. Therefore, repeated measurements are serially correlated from same subject (within-subject variation) and there is also variation between subjects (between-subject variation). The serial correlation and the between-subject variation must be taken into account to make proper inference on covariate effects (Diggle et al., 2002). However, estimation of the covariance matrix is challenging because of many parameters and positive definiteness of the matrix. To overcome these limitations, we propose autoregressive moving average Cholesky decomposition (ARMACD) for the linear mixed models. The ARMACD allows a class of flexible, nonstationary, and heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the random effects covariance matrix. We analyze a real dataset to illustrate our proposed methods.

• Computers and Concrete
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• v.13 no.5
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• pp.659-677
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• 2014

A Bayesian response surface updating procedure is applied in order to update the parameters of the covariance function of a random field for concrete properties based on a limited number of available measurements. Formulas as well as a numerical algorithm are presented in order to update the parameters of response surfaces using Markov Chain Monte Carlo simulations. The parameters of the covariance function are often based on some kind of expert judgment due the lack of sufficient measurement data. However, a Bayesian updating technique enables to estimate the parameters of the covariance function more rigorously and with less ambiguity. Prior information can be incorporated in the form of vague or informative priors. The proposed estimation procedure is evaluated through numerical simulations and compared to the commonly used least square method.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.1
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• pp.36-43
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• 2009

In this study, the impact of the covariance between the baselines on the network-based GPS positioning is analyzed. For the analysis, the multi-baseline solutions with properly modeled covariance between the baselines and the combined solutions from the single-baseline solutions are obtained, respectively. Then, the accuracies of both solutions are evaluated in terms of coordinate residuals, i.e., the differences between the positioning solutions and the published stations' coordinates. The results indicate that the positioning accuracy in static mode depends much on the geometry of GPS satellites rather than the proper modeling of covariance between the baselines. Also, slight but negligible improvement in positioning accuracy is observed in static solutions. Therefore, one may use combined solutions as an alternative to multi-baseline solutions for the network-based GPS positioning. However, multi-baseline solution with properly modeled covariance between the baselines is recommended to use especially for the applications to detect very small displacement, i.e., deformation of the building or bridge.

• Journal of Environmental Science International
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• v.22 no.1
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• pp.119-129
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• 2013

We compared equilibrium evaporation($E_{equili}$) eddy-covariance($E_{eddy}$) with soil moisture data($E_{SMseries}$) which were measured with a 2 hours sampling interval at three points for a humid forest hillslope from May 5th to May 31th in 2009. Accumulations of $E_{eddy}$, $E_{equili}$ for the study period were estimated as 2.52, 3.28 mm and those of $E_{SMseries}$ were ranged from 1.91 to 2.88 mm. It suggested that the eddy-covariance method considering the spatial heterogeneity of soil evaporation is useful to evaluate the soil evaporation. Method A, B and C were proposed using mean meterological data and daily moisture variation and the computations were compared to eddy-covariance method and equilibrium evaporation. The methods using soil moisture data can describe the variations of soil evaporation from eddy-covariance through simple moving average analysis. Method B showed a good matched with eddy-covariance method. This indicated that Dry Surface Layer (DSL) at 14:00 which was used for method B is important variable for the evaluation of soil evaporation. The total equilibrium evaporation was not significantly different to those of the others. However, equilibrium evaporation showed a problem in estimating soil evaporation because the temporal tendency of $E_{equili}$ was not related with the those of the other methods. The improved understanding of the soil evaporation presented in this study will contribute to the understandings of water cycles in a forest hillslope.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1477-1484
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• 2015

This paper proposes a robust method for object tracking based on Kalman filters algorithm and covariance matrix. As a feature of the object to be tracked, covariance matrix ensures the continuity of the moving target tracking in the image frames because the covariance is addressed spatial and statistical properties as well as the correlation properties of the features, despite the changes of the form and shape of the target. However, if object moves faster than operation time, real time tracking is difficult. In order to solve the problem, Kalman filters are used to estimate the area of the moving object and covariance matrices as a feature vector are compared with candidate regions within the estimated Kalman window. The results show that the tracking rate of 96.3% achieved using the proposed method.

• Journal of Advanced Navigation Technology
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• v.8 no.1
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• pp.26-37
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• 2004

Consistent and realistic error covariance information is important for position estimation, error analysis, fault detection, and integer ambiguity resolution for differential GNSS. In designing a position domain carrier-smoothed-code filter where incremental carrier phases are used for time-propagation, formulation of consistent error covariance information is not easy due to being bounded and temporal correlation of propagation noises. To provide consistent and correct error covariance information, this paper proposes two recursive filter algorithms based on carrier-smoothed-code techniques: (a) the stepwise optimal position projection filter and (b) the stepwise unbiased position projection filter. A Monte-Carlo simulation result shows that the proposed filter algorithms actually generate consistent error covariance information and the neglection of carrier phase noise induces optimistic error covariance information. It is also shown that the stepwise unbiased position projection filter is attractive since its performance is good and its computational burden is moderate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.364-370
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• 2011

This paper propose a novel method for tracking moving object based on covariance matrix and Riemannian Manifolds. With image backgrounds continuously changed, we use the covariance matrices to extract features for tracking nonrigid object undergoing transformation and deformation. The covariance matrix can make fusion of different types of features and has its small dimension, therefore we enable to handle the spatial and statistical properties as well as the component correlation. The proposed method can estimate the position of the moving object by employing the covariance matrix of object region as a feature vector and comparing the candidate regions. Rimannian Geometry is efficiently adapted to object deformation and change of shape and improve the accuracy by using geodesic distance to predict the estimated position with the minimum distance. The experimental results have shown that the proposed method correctly tracked the moving object.