• 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.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.11
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• pp.516-520
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• 2003

A novel dual beam heterodyne Laser Doppler Vibrometer (LDV) in conjunction with FM demodulators, which utilizes a residual beam to eliminate the perturbationdue due to the vibrometer body vibration without any external reference surface, has been developed. Residual laser beam from the beam splitter is used to pick up the vibration of damper, which is mounted in the vibrometer, and combined with reference beam at the photodetector. The output signal of this detector and main signal are processed to extract the object vibration, using a least mean square adaptive algorithm. It is shown experimentally that the body vibration of 1-5 Hz can be effectively removed from the measured signal using DSP technology to extract unperturbed 100 Hz original signal.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1045-1050
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• 1988

The problem of applying the linear integral filter in analysis and parameter estimation of linear continuous systems is discussed. A discrete-time model, which is equivalent to that obtained using the bilinear z transformation, is derived and employed to predict system output. It is shown that the output error can be controlled through the sampling interval. In order to obtain unbiased estimates, an instrumental variable (IV) method is proposed, where the instrumental variables are constituted using adaptive filtering. Some problems on implementation of the recursive IV algorithm are discussed. Both theoretical analysis and simulation study are given to illustrate the proposed methods.