• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.683-687
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• 2007

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and Closed-Form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-Form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a Self-Tuning Weighted Least Square AOA algorithm that is a modified version of the conventional Closed-Form solution. In order to estimate the error covariance matrix as a weight, a two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.485-489
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• 2006

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and closed-form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a self-tuning weighted least square AOA algorithm that is a modified version of the conventional closed-form solution. In order to estimate the error covariance matrix as a weight, two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.595-611
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• 2016

We use nonlinear regression models (such as the Hill Model) when we analyze data in toxicology and/or pharmacology. In nonlinear regression models an estimator of parameters and estimation of measurement about uncertainty of the estimator are influenced by the variance structure of the error. Thus, estimation methods should be different depending on whether the data are homoscedastic or heteroscedastic. However, we do not know the variance structure of the error until we actually analyze the data. Therefore, developing estimation methods robust to the variance structure of the error is an important problem. In this paper we propose a method to estimate parameters in nonlinear regression models based on a preliminary test. We define an estimator which uses either the ordinary least square estimation method or the iterative weighted least square estimation method according to the results of a simple preliminary test for the equality of the error variance. The performance of the proposed estimator is compared to those of existing estimators by simulation studies. We also compare estimation methods using real data obtained from the National Toxicology program of the United States.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1171-1180
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• 2010

It is well known that the asymptotic convergence rates of nonparametric regression estimator gets worse as the dimension of covariates gets larger. One possible way to overcome this problem is reducing the dimension of covariates by using single index models. Two coefficient estimation methods in single index models are introduced. One is semiparametric least square estimation method, which tries to find approximate solution by using iterative computation. The other one is weighted average derivative estimation method, which is non-iterative method. Both of these methods offer the parametric convergence rate to normal distribution. However, practical comparison of these two methods has not been done yet. In this article, we compare these methods by examining the variances of estimators in various models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4C
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• pp.254-264
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• 2011

In this paper, we propose two algorithm for improving the performance of wireless localization(Trilateration and Least Square) based on the range based approach method in indoor environment using RSSI for ranging distance. we propose a method to discriminate the case that has relatively large estimation errors in trilateration using Heron''s formula for the volume of a tetrahedron. And we propose the algorithm to process the discriminated types of distance using the absolute value calculated by Heron''s formula. In addition, we propose another algorithm for the case of which the number of anchor nodes larger than three. In this case, Residual Weighting Factor(RWGH) improves the performance of Least Square. However, RWGH requires many number of calculations. In this paper, we propose Iterative Weighted Centroid Algorithm(IWCA) that has better performance and less calculation than RWGH. We show the improvement of performance for two algorithms and the combination of these algorithm by using simulation results.