• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.41-43
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• 2004

This paper is concerned with attitude estimation using low cost, small-sized accelerometers and gyroscopes. A two step extended Kalman filter is proposed, which adaptively compensates external acceleration. External acceleration is the main source of estimation error. In the proposed filter, direction of external acceleration is estimated. According to the estimated direction, the accelerometer measurement covariance matrix of the two step extended Kalman filter is adjusted. The proposed algorithm is verified through experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.51-55
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• 1997

This study is devote to developing navigation filter for detecting sensor failure in multi-sensor navigation system. In multi-sensor navigation system, Kalman filter is generally used to fuse data of each sensors. Sensor failure is fatal in case that the sensor is used as external measurement of Kalman filter therefore detection and recovery of sensor failure is one the important feature of navigation filter. Generally each sensors have its specific feature in measuring navigational information. Fuzzy theory is proposed to detect external sensor failure and provide valid external measurement to Kalman filter avoiding filter divergence and instability. This idea is applied to Autonomous Underwater Vehicle(AUV) which has two navigation sensor i. e self contained inertial sensor and acoustic external sensor. 2 dimensional simulation result shows acceptable failure detection and recovery

• Journal of the Korea Institute of Military Science and Technology
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• v.1 no.1
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• pp.201-210
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• 1998

Inertial Navigation System(INS) can provide the vehicle position and velocity information using inertial sensor outputs without the use of external aids. Unfortunately INS navigation error increases with time due to inertial sensor errors, and therefore it is desirable to combine INS with external aids such as GPS, TACAN, OMEGA, and etc.. In this paper we propose an integration algorithm of commercial GPS/GLONASS and INS where an adaptive filter for signal processing of GPS/GLONASS receiver and the 12th order Kalman filter for aided strapdown INS(SDINS) we employed. Simulation results show that the proposed adaptive filter can effectively remove a randomly occurring abrupt jump due to sudden corruption of the received satellite signal and that the Kalman filter performs satisfactorily.

• Smart Structures and Systems
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• v.17 no.6
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• pp.903-915
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• 2016

The classical Kalman filter (KF) provides a practical and efficient state estimation approach for structural identification and vibration control. However, the classical KF approach is applicable only when external inputs are assumed known. Over the years, some approaches based on Kalman filter with unknown inputs (KF-UI) have been presented. However, these approaches based solely on acceleration measurements are inherently unstable which leads poor tracking and so-called drifts in the estimated unknown inputs and structural displacement in the presence of measurement noises. Either on-line regularization schemes or post signal processing is required to treat the drifts in the identification results, which prohibits the real-time identification of joint structural state and unknown inputs. In this paper, it is aimed to extend the classical KF approach to circumvent the above limitation for real time joint estimation of structural states and the unknown inputs. Based on the scheme of the classical KF, analytical recursive solutions of an improved Kalman filter with unknown excitations (KF-UI) are derived and presented. Moreover, data fusion of partially measured displacement and acceleration responses is used to prevent in real time the so-called drifts in the estimated structural state vector and unknown external inputs. The effectiveness and performance of the proposed approach are demonstrated by some numerical examples.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.6
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• pp.829-836
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• 2006

The reaction rate of denitrification is primarily affected by the utilization of organics that are usually limited in the anoxic period in a sequencing batch reactor. It is necessary to add an extemal carbon source for sufficient denitrification. An adaptive model of state-space based on the extended Kalman filter is applied to manipulate the dosage rate of extemal carbon automatically. Control strategies for denitrification have been studied to improve control performance through simulations. The normal control strategy of the constant set-point results in the overdosage of external carbon and deterioration of water quality. To prevent the overdosage of external carbon, improved control strategies such as the constrained control action, variable set-point, and variable set-point after dissolved oxygen depletion are required. More stable control is obtained through the application of the variable set-point after dissolved oxygen depletion. The converging value of the estimated denitrification coefficient reflects conditions in the reactor.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.11-19
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• 2014

In this paper, an external torque estimation problem in one-degree-of-freedom (1-DOF) flexible-joint robot equipped with a joint-torque sensor is revisited. Since a sensor torque from the joint-torque sensor is distorted by two dynamics having a spring connection, i.e., motor dynamics and link dynamics of a flexible-joint robot, a model-based estimation, rather than a simple linear spring model, should be required to extract external torques accurately. In this paper, an external torque estimation algorithm for a 1-DOF flexible-joint robot is proposed. This algorithm estimates both an actuating motor torque from the motor dynamics and an external link torque from the link dynamics simultaneously by utilizing the flexible-joint robot model and the Kalman filter estimation based on random-walk model. The basic structure of the proposed algorithm is explained, and the performance is investigated through a custom-designed experimental testbed for a vertical situation under gravity.

• The Journal of the Acoustical Society of Korea
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• v.20 no.1E
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• pp.25-30
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• 2001

The unstable signature that is defined as frequency change with respect to the time or frequency modulation, is caused by the external loading variation in specific machinery component and Doppler shift etc. In this study, we analyze the generation mechanism of the unstable signature and apply the Extended Kalman filter (EKF) algorithm for its detection. The performance of Extended Kalman Filter is examined for numerical and measured signals and the results show its validity for unstable signature detection.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.81-81
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• 2000

We use camera to apply human vision system in measurement. To do that, we need to know about camera parameters. The camera parameters are consisted of internal parameters and external parameters. we can fix scale factor&focal length in internal parameters, we can acquire external parameters. And we want to use these parameters in automatically driven vehicle by using camera. When we observe an camera parameters in respect with that the external parameters are important parameters. We can acquire external parameter as fixing focal length&scale factor. To get lane coordinate in image, we propose a lane detection filter. After searching lanes, we can seek vanishing point. And then y-axis seek y-sxis rotation component(${\beta}$). By using these parameter, we can find x-axis translation component(Xo). Before we make stepping motor rotate to be y-axis rotation component(${\beta}$), '0', we estimate image coordinates of lane at (t+1). Using this point, we apply this system to Kalman filter. And then we calculate to new parameters whick make minimum error.

• Journal of the Society of Naval Architects of Korea
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• v.38 no.4
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• pp.30-38
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• 2001

The current is considered in the conventional manoeuvering equation. This equation is represented as the nonlinear state and measurement equations in which external forces and the direction and the velocity of current are augmented as that variables. The external forces are modeled as the third-order Gauss-Markov processes and the direction and the velocity of current are assumed to be constant. The augmented state variables are estimated with extended Kalman-Bucy filter and the fixed-interval smoother. While Hwang estimated motion state variables, hydrodynamic coefficients and the current variables simultaneously by using extended Kalman filter, external forces of surge, sway and yaw and the direction and the velocity of current are the only parameters to be estimated in the estimation-before-modeling method. The current variables are satisfactorily estimated in simulation process where the measurement noise is present.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.3
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• pp.175-181
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• 2020

For the success of a structural integrity management, it is essential to acquire structural response data at some critical locations with limited number of sensors. In this study, the structural response of numerical model was estimated by data fusion approach based on the Kalman filter known as stochastic recursive filter. Firstly, transient direct analysis was conducted to calculate the acceleration and strain of the numerical standing beam model, then the noise signals were mixed to generate the numerical measurement signals. The acceleration measurement signal was provided to the Kalman filter as an information on the external load, and the displacement measurement, which was transformed from the strain measurement by using strain-displacement conversion relationship, was provided into the Kalman filter as an observation information. Finally, the Kalman filter estimated the displacement by combining both displacements calculated from each numerically measured signal, then the estimated results were compared with the results of the transient direct analysis.