• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.620-623
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• 1997

This paper is concerned with the design of a suboptimal Kalman filter with robust state estimation performance for system models represented in the state space, which are subjected to parameter uncertainties in both the state and measurement matrices. Under the assumption that the uncertain system is quadratically stable, if the augmented system composed of the uncertain system and the filter is controllable, the proposed filter can provide the upper bound of the estimation error variance for all admissible uncertain parameters. This upper bound can be represented as the convex function of a parameter introduced in the design procedure, and the optimized upper bound of the estimation error variance can also be found via the optimization of this convex function.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.5
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• pp.43-52
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• 2003

Two methods which estimate manoeuvring derivatives in the model of hydrodynamic force and moment acting on a manoeuvring ship using sea trial data were compared. One is the widely used parameter estimation method by using the Extended Kalman Filter (EKF), which estimates state variables of linearized state space model at every instant after dealing with the coefficients as the augmented state variables. The other one is the Estimation-Before-Modeling (EBM) technique, so called the two-step method. In the first step, hydrodynamic force of which dynamic model is assumed the third-order Gauss-Markov process is estimated along with motion variables by the EKF and the modified Bryson-Frazier smoother. Then, in the next step, manoeuvring derivatives are identified through the regression analysis. If the exact structure of hydrodynamic force could be known, which was an ideal case, the EKF method would be regarded as being more superior compared to the EBM technique. However the EBM technique was more robust than the EKF method from a realistic point of view where the assumed model structure was slightly different from the real one.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.975-978
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• 2010

This paper presents a Kalman filter based modeling algorithm for autonomous robots. State of the robot systems is measured by using embedded sensors and then carried to a host computer via ubiquitous sensor network (USN). We settle a linear state space motion equation for unknown system dynamics and modify a popular Kalman filter algorithm in deriving suitable parameter estimation mechanism. We conduct real-time experiment to test our proposed modeling algorithm where velocity state of the constructed robot is used as system observation.

• Journal of Astronomy and Space Sciences
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• v.26 no.1
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• pp.31-46
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• 2009

An Unscented Kalman Filter (UKF) for estimation of the attitude and rate of a spacecraft using only magnetometer vector measurement is developed. The attitude dynamics used in the estimation is the nonlinear Euler's rotational equation which is augmented with the quaternion kinematics to construct a process model. The filter is designed for small satellite in low Earth orbit, so the disturbance torques include gravity-gradient torque, magnetic disturbance torque, and aerodynamic drag torque. The magnetometer measurements are simulated based on time-varying position of the spacecraft. The filter has been tested not only in the standby mode but also in the detumbling mode. Two types of actuators have been modeled and applied in the simulation. The PD controller is used for the two types of actuators (reaction wheels and thrusters) to detumble the spacecraft. The estimation error converged to within 5 deg for attitude and 0.1 deg/s for rate respectively when the two types of actuators were used. A joint state parameter estimation has been tested and the effect of the process noise covariance on the parameter estimation has been indicated. Also, Monte-Carlo simulations have been performed to test the capability of the filter to converge with the initial conditions sampled from a uniform distribution. Finally, the UKF performance has been compared to that of the EKF and it demonstrates that UKF slightly outperforms EKF. The developed algorithm can be applied to any type of small satellites that are actuated by magnetic torquers, reaction wheels or thrusters with a capability of magnetometer vector measurements for attitude and rate estimation.

• Ocean Systems Engineering
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• v.9 no.4
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• pp.391-407
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• 2019

Tracking the location (position) of a surface or underwater marine vehicle is important as part of guidance and navigation. While the Global Positioning System (GPS) works well in an open sea environment but its use is limited whenever testing scaled-down models of such vehicles in the laboratory environment. This paper presents the design, development and implementation of a low energy ultrasonic augmented single beacon-based localization technique suitable for such requirements. The strategy consists of applying Extended Kalman Filter (EKF) to achieve location tracking from basic dynamic distance measurements of the moving model from a fixed beacon, while on-board motion sensor measures heading angle and velocity. Iterative application of the Extended Kalman Filter yields x and y co-ordinate positions of the moving model. Tests performed on a free-running ship model in a wave basin facility of dimension 30 m by 30 m by 3 m water depth validate the proposed model. The test results show quick convergence with an error of few centimeters in the estimated position of the ship model. The proposed technique has application in the real field scenario by replacing the ultrasonic sensor with industrial grade long range acoustic modem. As compared with the existing systems such as LBL, SBL, USBL and others localization techniques, the proposed technique can save deployment cost and also cut the cost on number of acoustic modems involved.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2526-2531
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• 2005

A practical pinch torque estimator based on the Kalman filter is proposed for low-cost anti-pinch window control systems. To obtain the accurate angular velocity from Hall-effect sensor measurements, the angular velocity calculation algorithm is executed with additional procedures for removing the measurement noises. Apart from the previous works using the angular velocity estimates and torque estimates for detecting the pinched condition, the torque rate is augmented to the system model and the proposed pinch estimator is derived by applying the steady-state Kalman filter recursion to the model. The motivation of this approach comes from the idea that the bias errors in torque estimates due to the motor parameter uncertainties can be almost eliminated by introducing the torque rate state. For detecting the pinched condition, a systematic way to determine the threshold level of the torque rate estimates is also suggested via the deterministic estimation error analysis. Simulation results are given to certify the pinch detection performance of the proposed algorithm and its robustness against the motor parameter uncertainties.

• Structural Engineering and Mechanics
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• v.59 no.6
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• pp.1037-1054
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• 2016

Structural parameter evaluation and external force estimation are two important parts of structural health monitoring. But the structural parameter identification with limited input information is still a challenging problem. A new simultaneous identification method in time domain is proposed in this study to identify the structural parameters and evaluate the external force. Each sampling point in the time history of external force is taken as the unknowns in force evaluation. To reduce the number of unknowns for force evaluation the time domain measurements are divided into several windows. In each time window the structural excitation is decomposed by orthogonal polynomials. The time-variant excitation can be represented approximately by the linear combination of these orthogonal bases. Structural parameters and the coefficients of decomposition are added to the state variable to be identified. The extended Kalman filter (EKF) is augmented and selected as the mathematical tool for the implementation of state variable evaluation. The proposed method is validated numerically with simulation studies of a time-invariant linear structure, a hysteretic nonlinear structure and a time-variant linear shear frame, respectively. Results from the simulation studies indicate that the proposed method is capable of identifying the dynamic load and structural parameters fairly accurately. This method could also identify the time-variant and nonlinear structural parameter even with contaminated incomplete measurement.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.4
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• pp.43-48
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• 2015

In this study, a method to enhance the dynamic range of seismic sensor is proposed. The low frequency noise included in the measurement of seismic sensor is modelled as an ARMA(Auto Regressive Moving Average) model and the order and parameters of the model are identified through system identification method. The identified noise model is augmented into Kalmman filter which estimate seismic signal from sensor measurement. The proposed method is applied to a newly developed seismic sensor which is MEMS based 3-axis accelerometer type. The experiment show that the proposed method can enhance the dynamic range compared to the simple low pass filtering.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.107-116
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• 2013

We proposed the WSN monitoring system applied the augmented reality to visualize effectively an indoor WSN. To implement system, we used wireless sensor network, indoor location determination, location-based augmented reality technology. First, we composed the wireless sensor networks indoors and implement web server and then get data from server DB using Android phones. Then, we obtained the (x, y) coordinates using the triangulation method from RSSI of three point of the strongest signal strength of the AP's. Also, we adjusted coordinates using the Kalman filter. Finally, we inserted the adjusted coordinates to the latitude and the longitude of the Mixare that use the GPS signal, and we got location of user and wireless sensor in the server DB. After that, we implemented augmented reality system using the android phone and wireless sensor location and data and real life image.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.36.1-36.1
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• 2008

An Unscented Kalman Filter(UKF) for estimation of attitude and rate of a spacecraft using only magnetometer vector measurement is presented. The dynamics used in the filter is nonlinear rotational equation which is augmented by the quaternion kinematics to construct a process model. The filter is designed for low Earth orbit satellite, so the disturbance torques include gravity-gradient torque, magnetic disturbance torque, and aerodynamic drag. The magnetometer measurements are simulated based on time-varying position of the spacecraft. The filter has been tested not only in the standby mode but also in the detumbling mode. To stabilize the attitude, linear PD controller is applied and the actuator is assumed to be thruster. A Monte-Carlo simulation has been done to guarantee the stability of the filter performance to the various initial conditions. The UKF performance is compared to that of EKF and it reveals that UKF outperforms EKF.