• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.274-279
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• 1998

For the accurate generation of DEMs, the determination of match quality in adaptive least square matching algorithm is significantly important. Traditionally, only the degree of convergence of a solution matrix in least squares estimation has been considered for the determination of match quality. It is, however, not enough to determine the true match quality. This paper reports two approaches of match quality determination based on adaptive least square correlation : the conventional if-then logic approaches with scene geometry and correlation as additional quality measures; and, the fuzzy logic approaches. Through these, accurate decision of match quality will minimize the number of blunder and maximize the number of exact match. The proposed methods have been tested on JERS and SPOT images and the results show good performance.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.20 no.4
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• pp.1-6
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• 2012

The accurate information of mass properties is required for the precise control of the spacecraft. The mass properties, mass and inertia, are changeable by some reasons such as consumption of propellant, deployment of solar panel, sloshing, environmental effect, etc. The gyro-based attitude data including noise and bias reduces the control accuracy so it needs to be compensated for improvement. This paper introduces a real-time inertia estimation method for the attitude determination of STSAT-3, Korea Science Technology Satellite. In this method we first filter the gyro noise with the Extended Kalman Filter(EKF), and then estimate the moment of inertia by using the filtered data from the EKF based on the Recursive Least Square(RLS).

• Journal of Power Electronics
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• v.18 no.2
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• pp.492-501
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• 2018

In the rotor-flux oriented control used in induction motors, the electrical parameters of the motors should be identified. Among these parameters, the mutual inductance and rotor resistance should be accurately tuned for better operations. However, they are more difficult to identify than the stator resistance and stator transient inductance. The rotor resistance and mutual inductance can change in operations due to flux saturation and heat generation. When detuning of these parameters occurs, the performance of the control is degenerated. In this paper, a novel method for the concurrent identification of the two parameters is proposed based on recursive least square estimation and model reference adaptive control.

• The Transactions of the Korean Institute of Power Electronics
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• v.5 no.6
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• pp.575-583
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• 2000

In this paper, a novel estimation alogrithm of mechanical parameters in two-mass system proposed. The inertia of a load and a motor and the stiffness are estimated by using RLS(Recursive Least Square) algorithm and acceleration information of motor. The effectiveness of the proposed scheme is verified with simulation and experiments results.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.101-108
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• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• 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.

• Structural Engineering and Mechanics
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• v.34 no.1
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• pp.1-14
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• 2010

The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthesize the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The capability of this inverse method are demonstrated in the input force estimation cases of the plate structure system. The proposed algorithm is further compared by alternating between the constant and adaptive weighting factors. The results show that this method has the properties of faster convergence in the initial response, better target tracking capability, and more effective noise and measurement bias reduction.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.249-254
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• 2014

Wireless LAN system is evolving toward high-speed data transmission and more accurate channel estimation is necessarily required to improve communication performance. The PLCP preamble field in IEEE 802.11 based wireless MODEM consists of ten short symbols and two long symbols and is used for synchronization and channel estimation. The existing least square (LS) channel estimation is based on only two long training symbols. After estimating channel response separately by using each long training symbol, the final channel estimation is obtained by the average of each estimation. In this paper, a new channel estimation algorithm is presented to improve the performance of the existing LS channel estimation algorithm. From the fact that the short training symbol consists of 12 non-zero subcarriers, it gives us a clue of being able to additionally estimate at least one fourth of channel coefficients. The new LS algorithm performs channel estimation based on both two long training symbols and a short training symbol. The proposed LS algorithm shows a little bit performance improvement over the existing LS estimation and it will be able to be applied to the IEEE 802.11p WAVE system.

• Journal of Hydrogen and New Energy
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• v.27 no.1
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• pp.36-41
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• 2016

The development need of new renewable energy is more and more important to resolve exhaustion of chemical fuels and environmental pollution. Polymer electrolyte membrane fuel cell has been widely studied to the extent that it can be used commercially. But there are many problems to be solved. One of them is to enhance the stability of fuel cell stacks. This paper proposes a new fault diagnosis method using Least Square Method (LSM) which is one of parameter estimation methods. The proposed method extracts equivalent circuit parameters from on-line measurements. Parameters of the circuit are estimated according to normal and abnormal states using simulation. The variation of parameters estimated in each states enables the estimation of state in fuel cells. Thus the LSM presented can be a suitable on-line parameter estimation method in PEMFC.

• Wind and Structures
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• v.11 no.1
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• pp.19-33
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• 2008

This research investigates the adaptive input estimation method applied to the multilayer shearing stress structure. This method is to estimate the values of wind load inputs by analyzing the active reaction of the system. The Kalman filter without the input term and the adaptive weighted recursive least square estimator are two main portions of this method. The innovation vector can be produced by the Kalman filter, and be applied to the adaptive weighted recursive least square estimator to estimate the wind load input over time. This combined method can effectively estimate the wind loads to the structure system to enhance the reliability of the system active performance analysis. The forms of the simulated inputs (loads) in this paper include the periodic sinusoidal wave, the decaying exponent, the random combination of the sinusoidal wave and the decaying exponent, etc. The active reaction computed plus the simulation error is regard as the simulated measurement and is applied to the input estimation algorithm to implement the numerical simulation of the inverse input estimation process. The availability and the precision of the input estimation method proposed in this research can be verified by comparing the actual value and the one obtained by numerical simulation.