• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.807-810
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• 2015

In this paper, a finite impulse response(FIR) fixed-lag smoother is proposed for discrete-time nonlinear systems. If the actual state trajectory is sufficiently close to the nominal state trajectory, the nonlinear system model can be divided into two parts: The error-state model and the nominal model. The error state can be estimated by adapting the optimal time-varying FIR smoother to the error-state model, and the nominal state can be obtained directly from the nominal trajectory model. Moreover, in order to obtain more robust estimates, the linearization errors are considered as a linear function of the estimation errors. Since the proposed estimator has an FIR structure, the proposed smoother can be expected to have better estimation performance than the IIR-structured estimators in terms of robustness and fast convergence. Additionally the proposed method can give a more general solution than the optimal FIR filtering approach, since the optimal FIR smoother is reduced to the optimal FIR filter by setting the fixed-lag size as zero. To illustrate the performance of the proposed method, simulation results are presented by comparing the method with an optimal FIR filtering approach and linearized Kalman filter.

• Proceedings of the KIEE Conference
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• 1986.07a
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• pp.140-144
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• 1986

In this paper an FIR(finite impulse response) filter and smoother are introduced for discrete time-invariant state-space models with driving noises. The FIR structure not only quarantees the BIBO stability and the robustness to parameter changes but also improves the filter divergence problem. It is shown that the impulse responses of the FIR filter and the smoother are obtained by Riccati-type difference equations and that they are to be time-invariant and reduced to very simple forms. For implementational purpose, recursive forms of the FIR filler and smoother are derived with each other used as the adjoint variable.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.378-384
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• 2017

In this paper, an efficient short-time Fourier analysis method for the quasi-periodic signals is proposed via an optimal fixed-lag finite impulse response (FIR) smoother approach using a receding horizon scheme. In order to deal with time-varying Fourier coefficients (FCs) of quasi-periodic signals, a state space model including FCs as state variables is augmented with the variants of FCs. Through an optimal fixed-lag FIR smoother, FCs and their increments are estimated simultaneously and combined to produce final estimates. A lag size of the optimal fixed-lag FIR smoother is chosen to minimize the estimation error. Since the proposed estimation scheme carries out the correction process with the estimated variants of FCs, it is highly probable that the smaller estimation error is achieved compared with existing approaches not making use of such a process. It is shown through numerical simulation that the proposed scheme has better tracking ability for estimating time-varying FCs compared with existing ones.

• International Journal of Ocean System Engineering
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• v.2 no.3
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• pp.139-150
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• 2012

This paper presents an improved guidance algorithm for autonomous underwater vehicles (AUV) in 3D space for generating smoother vehicle turn during the course change at the way-points. The way-point guidance by the line-of-sight (LOS) method has been modified for correcting the reference angles to achieve minimal calculation and smoother transition at the way-points. The algorithm has two phases in which the first phase brings the vehicle to converge to a distance threshold point on the line segment connecting the first two way-points and the next phase generates an angular path with smoother transition at the way-points. Then the desired angles are calculated from the reference and correction angles. The path points are regularly parameterized in the spherical coordinates and mapped to the Cartesian coordinates. The proposed algorithm is found to be simple and can be used for real time implementation. The details of the algorithm and simulation results are presented.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.574-574
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.575-583
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.305-310
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• 2002

The measured rate of the tracking sensor becomes biased under some operational situation. For a highly maneuverable aircraft in 3D space, the target dynamics changes from time to time, and the Kalman filter using position measurement only can not be used effectively to reject the rate measurement bias error. To cope with this problem, we present a new algorithm which incorporate FIR-type filter and FIR-type fixed-lag smoother, and demonstrate that it has the optimal performance in terms of both estimation accuracy and response time through an application example to the anti-aircraft gun fire control system(AAGFCS).

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.95-97
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• 2007

In this paper, we propose the optimal lag size which is optimize the performance of the fixed-lag minimum variance FIR smoother. Since the performance of estimation is represented with two Riccati equation and the nonlinear equation of lag size, it is difficult to obtain the optimal lag size. Therefore, we consider the optimal lag size for the scalar system and the numerical example is provided to demonstrate the proposed algorithm.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.31-38
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• 2003

A simple method is proposed to detect the number of change points and test the location and size of multiple change points with jump discontinuities in an otherwise smooth regression model. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Our proposed methodology is explained and applied to real data and simulated data.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.435-447
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• 2004

In additive nonparametric regression, Linton and Nielsen (1995) showed that the marginal integration when applied to the local linear smoother produces a rate-optimal estimator of each univariate component function for the case where the dimension of the predictor is two. In this paper we give new formulas for the bias and variance of the marginal integration regression estimators which are valid for boundary areas as well as fixed interior points, and show the local linear marginal integration estimator is in fact rate-optimal when the dimension of the predictor is less than or equal to four. We extend the results to the case of the local polynomial smoother, too.