• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.245-248
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• 2006

A particle filter is one of the most famous filters. The reason why the particle filter is widely used is that particle deals with the state estimation problem for not only linear models with Gaussian noise but also the non-linear models with non-Gaussian noise and it receives great attention from many engineering fields. In the point of view state estimator, particle filter is feedforward observer. According to the characteristic of dynamic system, the feedforward observer can estimate real state. However, the speed of convergence of feedforward observer between the actual state and the estimated state cannot be satisfied. Since the particle filter is a sort of feedforward observer, the convergence speed of particle filter is slow, and the particle filter cannot estimate actual state like particle collapse problem. In order to overcome the limitation of particle filter as a kind of feedfoward estimator, we propose a new particle filter which has feedback term, called particle filter with feedback. Our proposed method is analyzed theoretically and studied by computer simulation. Comparisons are made with other filtering mehod.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.77-87
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• 2001

Some asymptotic results on the local likelihood density estimator of Copas(1995) are derived when the locally parametric model has several parameters. It turns out that it has the same asymptotic mean squared error as that of Hjort and Jones(1996).

• The Journal of Asian Finance, Economics and Business
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• v.5 no.1
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• pp.11-16
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• 2018

This research examines the alternative ways of estimating the coefficient of non-diversifiable risk, namely beta coefficient, in Capital Asset Pricing Model (CAPM) introduced by Sharpe (1964) that is an essential element of assessing the value of diverse assets. The non-parametric methods used in this research are the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator). The Jackknife, the resampling technique, is also employed to validate the results. According to finance literature and common practices, these coecients have often been estimated using Ordinary Least Square (LS) regression method and monthly return data set. The empirical results of this research pointed out that the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator) performed much better than Ordinary Least Square (LS) in terms of eciency for large-cap stocks trading actively in the United States markets. Interestingly, the empirical results also showed that daily return data would give more accurate estimation than monthly return data in both Ordinary Least Square (LS) and robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator) regressions.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.646-647
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• 2005

A recently published paper demonstrates the utility of allowing delays in state estimators for linear systems with unknown inputs. In this note, we point out an error in the derivation of the estimator.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.231-234
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• 1996

This paper considers the output regulation problems on uncertain systems. Using NR-estimator(on-line), a family of equilibrium points for the uncertain system is computed. The state variables of the closed loop system track the average value of the obtained equilibrium manifold by dynamic state feedback control.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.347-350
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• 2005

This paper proposes a new tracking algorithm using the Kalman estimator based color SSD algorithm. The Kalman estimator includes the color information as well as the position and size of the face region in its state vector, to take care of the variation of skin color while faces are moving. Based on the estimated face position, the color SSD algorithm finds the face matching with the one in the previous frame even when the color and size of the face region vary. The features of a face region extracted by the color SSD algorithm are used to update the state of the Kalman estimator.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.201-207
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• 2006

A robust Kalman filtering method for uncertain stochastic systems is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state transition equation. The predictor and corrector of discrete Kalman filter are reformulated with the perturbation estimator. Successively, the state and perturbation estimation error dynamics and the corresponding error covariance propagation equations are derived as well. Finally we have the recursive algorithm of Combined Kalman Filter-Perturbation Estimator (CKF). The proposed combined Kalman filter-perturbation estimator has the property of integrating innovations and the adaptation capability to system uncertainties. A numerical example is shown to demonstrate the effectiveness of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1041-1044
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• 1988

This paper considers the design problem of adaptive filters based an the state-space models for linear discrete-time stationary stochastic signal processes. The adaptive state estimator consists of both the predictor and the sequential prediction error estimator. The discrete Chandrasakhar filter developed by author is employed as the predictor and the nonlinear least-squares estimator is used as the sequential prediction error estimator. Two models are presented for calculating the parameter sensitivity functions in the adaptive filter. One is the exact model called the linear innovations model and the other is the simplified model obtained by neglecting the sensitivities of the Chandrasekhar X and Y functions with respect to the unknown parameters in the exact model.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.252-255
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• 2014

In the case of long-stator linear drives, unlike rotative drives for which speed or position sensors are a single unit attached to the shaft, these sensors extend along the guideway. The position signal transmitted from maglev vehicle can't meet the need of the real-time propulsion control. In this paper the position estimator for propulsion inverter driving long stator linear synchronous motor (LSLSM) in high speed maglev train is proposed. In order to get the higher resolution of the position information transmitted from vehicle, Full order state observer is proposed for position estimator.

• Journal of Advanced Navigation Technology
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• v.2 no.1
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• pp.34-42
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• 1998

An adaptive state and input estimator for the tracking of a target with unknown randomly switching input is developed. In modeling the unknown inputs, it is assumed that the input sequence is governed by semi-Markov process. By incorporating the semi-Markov probability concepts into the Bayesian estimation theory, an effective adaptive state and input estimator which consists of parallel Kalman-type filters is obtained. Computer simulation results reveal that the proposed adaptive estimator have improved tracking performance in spite of the unknown randomly switching input.