• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.168-173
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• 2009

This paper investigates the estimation problem of vehicle velocity and propeller angular velocity on the underwater vehicle. Inspired by but different from a high-gain observer, the cascade observer features a cascade structure and adaptive observer gains. In doing so the cascade observer attempts to overcome some of the typical problems that may pose to a high-gain observer. As in the case of a high-gain observer, the cascade observer structure is simple and universal in the sense that it is independent of the system dynamics and parameters. A cascade observer is used for the estimation of velocity from measured position. In the 1st step of the observer, the output is estimated, and the 1st order derivative of measured output is estimated via the 2nd step of the observer. Also, nth order derivative of the output is estimated in the (n+1)th step of the observer. It is shown that the proposed observer guarantees globally asymptotical stability. By simulation results, the proposed observer scheme for the estimations of vehicle velocity and propeller angular velocity shows better performance than the scheme based on the existing observer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.76-77
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• 2022

In this paper, we propose a method to stabilize the 3D pose estimation result of a 3D volumetric data sequence by matching the pose estimation results from multi-view. Draw a circle centered on the volumetric model and project the model from the viewpoint at regular intervals. After performing Openpose 2D pose estimation on the projected 2D image, the 2D joint is matched to localize the 3D joint position. The tremor of 3D joints sequence according to the angular spacing was quantified and expressed in graphs, and the minimum conditions for stable results are suggested.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.8
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• pp.703-710
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• 2016

In robotics research, accurate estimation of current robot position is important to achieve motion control of a robot. In this research, we focus on a sensor fusion method to provide improved position estimation for a wheeled mobile robot, considering two different sensor measurements. In this case, we fuse camera-based vision and encode-based odometry data using Kalman filter techniques to improve the position estimation of the robot. An external camera-based vision system provides global position coordinates (x, y) for the mobile robot in an indoor environment. An internal encoder-based odometry provides linear and angular velocities of the robot. We then use the position data estimated by the Kalman filter as inputs to the motion controller, which significantly improves performance of the motion controller. Finally, we experimentally verify the performance of the proposed sensor fused position estimation and motion controller using an actual mobile robot system. In our experiments, we also compare the Kalman filter-based sensor fused estimation with two different single sensor-based estimations (vision-based and odometry-based).

• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.145-149
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• 2010

In this paper, we propose a kinematic approach to estimating the real-time moving object. A new scheme for a mobile robot to track and capture a moving object using images of a camera is proposed. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the active camera. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time path to capture the moving object, the linear and angular velocities are estimated and utilized. The experimental results of tracking and capturing of the target object with the mobile robot are presented.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.444-452
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• 2005

A new scheme for a mobile robot to track and capture a moving object using camera images is proposed. The moving object is assumed to be a point-object and is projected onto an image plane to form a geometrical constraint equation that provides the position data of the object based on the kinematics of the active camera. Uncertainties in position estimation caused by the point-object assumption are compensated for using the Kalman filter. To generate the shortest time path to capture the moving object, the linear and angular velocities are estimated and utilized. In this paper, the experimental results of the tracking and capturing of a target object with the mobile robot are presented.

• Journal of Power Electronics
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• v.12 no.5
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• pp.748-757
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• 2012

In a sensorless control system with a Permanent Magnet Synchronous Motor (PMSM), the angular position of the rotor flux can be estimated by a voltage equation. However, the estimated angle may be inaccurate due to various causes. In this paper, it was comprehensively analyzed how various causes affect the angle error. As a result of the analysis, an error equation intuitively describing these relationships was derived. The parameter errors of a PMSM and the non-ideal properties of the driving system were identified as error-causing factors. To demonstrate the validity of the error equation, PMSMs were tested at various operating points. The variations in angle errors could be well explained with the error equation.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.437-442
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• 2018

In this paper, we propose a method for estimating the position of a source in a closed form when a single source has coherently distributed property against a circular array. When a sound source reaches a sensor through multipath environments, it is seen as a distributed source and can be represented by four variables: the nominal azimuth, nominal elevation, azimuth angular spread, elevation angular spread. Therefore, it requires a lot of computation by a search method such as DSPE (Distributed Source Parameter Estimator). In this paper, we propose a method of estimating the nominal azimuth and elevation angle in a closed form using correlation function and least squares method for fast position estimation. In particular, if the source is assumed as Gaussian distribution model, the standard deviation is also estimated in a closed form. In the simulation, the validity of the proposed method is confirmed by comparing with the DSPE.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2033-2036
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• 2004

A novel approach to estimate the real time moving trajectory of an object is proposed in this paper. The object position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Kalman filter and neural networks are utilized. Since the Kalman filter needs to approximate a non-linear system into a linear model to estimate the states, there always exist errors as well as uncertainties again. To resolve this problem, the neural networks are adopted in this approach, which have high adaptability with the memory of the input-output relationship. Kohonen Network(Self-Organized Map) is selected to learn the motion trajectory since it is spatially oriented. The superiority of the proposed algorithm is demonstrated through the real experiments.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.2
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• pp.18-26
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• 1992

MR(Model Reduction) is presented in order to estimate the frequency of multiple sinusoids from the finite noisy data with the white or colored noises. MR, using the reduced rank models, is designed, appling the approximation of linear system to LP(Linear Prediction). The MR method is analyzed. Monte-carlo simulations are conducted for MR and Lp. The results are compared with in terms of mean, root-mean square and relative bias. MR eliminates effectevely the extremeous and exceptional poles appearing in LP and improves the accuracy of LP. Especially, MR gives promising results in short noisy measurements, low SNR's and colored noises. Power spectral density and angular frequency position are showed by figures, for examples. Finally, the new method is utilized to the communication and biomedical systems estimating the characteristics of the signal and the system identification modelling the dynamic systems from experimental data.