• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.133-138
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• 2019

The indoor navigation system using smart phone is a very important infrastructure technology for users' location based services in large indoor facilities. For this purpose, if the user can estimate the movement distance and direction by using the acceleration sensor and the gyro sensor built in the smartphone, the additional external environment is not necessary, which is a very useful technique. This paper deals with indoor navigation system technology that uses Pedestrian Dead Reckoning (PDR) technology and Kalman filter on a general smartphone and allows the user to trace the position while moving the smartphone in front of his chest. In particular, an extended Kalman filter was designed to estimate the direction of movement, and its performance was verified when walking at a constant speed.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.1065-1071
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• 2012

In this paper, we proposed a method for vehicle monitoring with trajectory reconstruction. For safety, it is important to monitor the driving habit of driver. Every year, many accidents occur due to the reckless driving of the driver. Continuous monitoring of the status of commercial vehicles is needed for safety through the entire path from start point to the destination. To monitor the reckless driving, we try to monitor the trajectory of the vehicle by using vehicle's lateral acceleration data. Compared with steering angle and lateral acceleration, these resemble each other. So, we find the relationship of steering angle and acceleration, and find the global direction of vehicle. We find the position of non-GPS section with EKF (External Kalman Filter) and reconstruct the whole trajectory during vehicle driving.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.1
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• pp.147-161
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• 2008

In this paper, fine attitude estimation using low-cost sensors for attitude pointing missions of spacecraft is addressed. Attitude kinematics and gyro models including bias models are in general utilized to estimate spacecraft attitude and angular rate. However, a linearized model and a transition matrix are derived in this paper from nonlinear spacecraft dynamics with external disturbances. A Kalman filtering technique is applied and offers relatively high estimation accuracy under dynamic uncertainties. The proposed approach is demonstrated using numerical simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.702-708
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• 2013

Underwater TRN (Underwater Terrain Referenced Navigation) estimates an underwater vehicle state by measuring a distance between the vehicle and undersea terrain, and comparing it with the known terrain database. TRN belongs to absolute navigation methods, which are used to compensate a drift error of dead reckoning measurements such as IMU (Inertial Measurement Unit) or DVL (Doppler Velocity Log). However, underwater TRN is different to other absolute methods such as USBL (Ultra-Short Baseline) and LBL (Long Baseline), because TRN is independent of the external environment. As a magnetic-field-based navigation, TRN is a kind of geophysical navigation. This paper develops an EKF (Extended Kalman Filter) formulation for underwater TRN. A filter propagation part is composed by an inertial navigation system, and a filter update is executed with echo-sounder measurement. For large-initial-error cases, an adaptive EKF approach is also presented, to keep the filter be stable. At the end, simulation studies are given to verify the performance of the proposed TRN filter. With simplified sensor and terrain database models, the simulation results show that the underwater TRN could support conventional underwater navigation methods.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.133-140
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• 2008

In this paper, a small and effective attitude estimation system for a humanoid robot was developed. Four small inertial sensors were packed and used for inertial measurements(3D accelerometer and three 1D gyroscopes.) An effective 3D pose estimation algorithm for low cost DSP using an extended Kalman filter was developed and evaluated. The 3D pose estimation algorithm has a very simple structure composed by 3 modules of a linear acceleration estimator, an external acceleration detector and an pseudo-accelerometer output estimator. The algorithm also has an effective switching structure based on probability and simple feedback loop for the extended Kalman filter. A special test equipment using linear motor for the testing of the 3D pose sensor was developed and the experimental results showed its very fast convergence to real values and effective responses. Popular DSP of TMS320F2812 was used to calculate robot's 3D attitude and translated acceleration, and the whole system were packed in a small size for humanoids robots. The output of the 3D sensors(pitch, roll, 3D linear acceleration, and 3D angular rate) can be transmitted to a humanoid robot at 200Hz frequency.

• Smart Structures and Systems
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• v.24 no.2
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• pp.209-221
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• 2019

The classical Kalman filter (KF) provides a practical and efficient way for state estimation. It is, however, not applicable when the external excitations applied to the structures are unknown. Moreover, it is known the classical KF is only suitable for linear systems and can't handle the nonlinear cases. The aim of this paper is to extend the classical KF approach to circumvent the aforementioned limitations for the joint estimation of structural states and the unknown inputs. On the basis of the scheme of the classical KF, analytical recursive solution of an improved KF approach is derived and presented. A revised form of observation equation is obtained basing on a projection matrix. The structural states and the unknown inputs are then simultaneously estimated with limited measurements in linear or nonlinear systems. The efficiency and accuracy of the proposed approach is verified via a five-story shear building, a simply supported beam, and three sorts of nonlinear hysteretic structures. The shaking table tests of a five-story building structure are also employed for the validation of the robustness of the proposed approach. Numerical and experimental results show that the proposed approach can not only satisfactorily estimate structural states, but also identify unknown loadings with acceptable accuracy for both linear and nonlinear systems.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.2
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• pp.106-111
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• 2009

Real-time video stabilization technology is used in correction for the camera vibrations of the hand-held camera by hand or fixed camera by external condition. This paper is about the counterplan to cope with the vibration of the movie generated by the large external cause relatively. we use the movie stabilization parameters with the phase correlation method based the DFT to get the displacements of the current frame to the reference frame. we use the kalman filter for the efficient and stable searching works on the phase correlation map and present the proper conditions for the real-time processing through the experiments. We propose the measure to evaluate the capability of the video stabilizer which is the standard deviation of the brightness of the center block. and compare the capability for the video sequences randomly shifted and the jittered video sequences obtained from camera.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1413-1422
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• 2012

Adaptive back stepping control technique may provide robust control characteristics under parameter perturbation caused by changing external condition. In order to synthesize a high-precision velocity controller for IPMSM(Interior Permanent Magnet Synchronous Motor) using this method, the period of control loop should be very small. However, because of the resolution of the encoder for speed measurement, control cycle is limited, which makes it difficult to improve the performance of the controller. This paper proposes a velocity controller design method based on nonlinear adaptive back-stepping method to accomplish fast and accurate performance. Here, an EKF(Extended Kalman Filter) method is incorporated for the estimation of the motor speed into the design of a speed controller using adapted back-stepping control technique. The performance of the proposed controller is demonstrated through simulation using PSIM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.911-913
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• 2013

The user's context awareness is important to the reliability of sensors data. The sensor data is constantly change to external temp, internal& external environment and vibration. This noise environment is affecting that the data collected information from sensors. Of course this method of digital filter and inference algorithm specifically request for the use of ripple noise and action inference. In this paper, experiment was a comparison of the KF(Kalman Filter) and WMAF(Weight Moving Average Filter) for noise decrease and distortion prevention according to user behavior. And, we compared the EWDF(Extended Weight Dual Filter) with several filer. In an experiment, in contrast to other filter, the proposed filter is robust in a noise-environment.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.10
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• pp.853-861
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• 2016

This paper deals with a new technique utilizing the angular speed of the reaction wheels to determine attitudes and angular rates for gyroless satellites. The suggested algorithm in this study is designed to determine the precise attitude and angular rates under actual space environments by the support of the angular speeds of reaction wheels based on the extended Kalman filter. Furthermore, the proposed approach is also designed to estimate not only the attitude and angular rates of spacecraft but the external disturbances. The numerical simulation was conducted for gyloless spacecraft installed with four reaction wheels of the pyramid-type configuration. The performance of the proposed algorithm is verified by using numerical simulations.