• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.6
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• pp.539-547
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• 2007

In this paper, development and tests of a real-time eye-tracker system are discussed. The tracker system tracks a user's gaze point through movement of eyes by means of vision-based pupil detection. The vision-based method has an advantage of detecting the exact positions of user's eyes. An infrared camera and a LED are used to acquire a user's pupil image and to extract pupil region, which was hard to extract with software only, from the obtained image, respectively. We develop a pupil-tracking algorithm with Kalman filter and grab the pupil images by using DSP(Digital Signal Processing) system for real-time image processing technique. The real-time eye-tracker system tracks the movements of user's pupils to project their gaze point onto a background image.

• Aerospace Engineering and Technology
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• v.5 no.2
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• pp.90-101
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• 2006

In this paper, the calibration parameters of the gyros and star hackers are estimated by using an on-orbit AOCS sensor calibration algorithm. The calibration algorithm was implemented by Kalman filter. In order to estimate gyro calibration parameters, the calibration algorithm requires calibration maneuver and it was analyzed whether the star trackers are protected by Sun, Moon and Earth or not. Also the star tracker calibration algorithm used the camera image information. This kinds of camera image information simulated ground control point and orbit information. The estimated accuracy of star tracker calibration parameters depends on camera image information.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1788-1789
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• 2011

In order to develop a one-axis gimbaled acoustic source tracker for mobile robots, a pseudo-linear direction of arrival(DOA) estimator is proposed using a linear ultrasonic sensor array. Under the assumption that the sensor measurement errors are negligible, a linear measurement model is derived using the linear prediction relation of the received sinusoidal acoustic signals. Applying the Kalman filtering technique for this model, the linear recursive DOA estimator is designed. For its linear recursive filter structure, it is preferable for real-time implementation on a commercial DSP. Through the experiments, the effectiveness of the suggested method is demonstrated.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.1
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• pp.31-40
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• 2010

For weapon cueing and Head-Mounted Display (HMD), it is essential to continuously estimate the motion of the helmet. The problem of estimating and predicting the position and orientation of the helmet is approached by fusing measurements from inertial sensors and stereo vision system. The sensor fusion approach in this paper is based on nonlinear filtering, especially expended Kalman filter(EKF). To reduce the computation time and improve the performance in vision processing, we separate the structure estimation and motion estimation. The structure estimation tracks the features which are the part of helmet model structure in the scene and the motion estimation filter estimates the position and orientation of the helmet. This algorithm is tested with using synthetic and real data. And the results show that the result of sensor fusion is successful.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.1
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• pp.170-182
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• 1999

In this paper, we present a tracking accuracy enhancement method by compensating the time delay of the video tracker in an EOTS. The proposed method has two functional parts, which can cope with the time delay of LOS and maneuvering target informations by Smith predictor and Kalman filter. So it can dramatically reduce the tracking error over conventional PI control or Smith predictor control. To verify the proposed method, various and extensive simulation and experimental results are given.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.110-114
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• 1995

This paper presents the on-board attitude determination algorithm for LEO (Low Earth Orbit) three-axis stabilized spacecraft. Two advanced star trackers and a three-axis Inertial Reference Unit (IRU) are assumed to be attitude sensors. The gyro in the IRU provides a direct measurement of the attitude rates. However, the attitude estimation error increases with time due to the gyro drift and noise. An update filter with measurements of star trackers and/or sun sensor is designed to update these gyro drift bias and to compensate the attitude error. Kalman Filter is adapted for the on-board update filter algorithm. Simulation results will be presented to investigate the attitude pointing performance.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.865-872
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• 2008

Spacecraft attitude estimation using the nonlinear unscented filter is addressed to fully utilize capabilities of the unscented transformation. To release significant computational load, an efficient technique is proposed by reasonably removing correlation between random variables. This modification introduces considerable reduction of sigma points and computational burden in matrix square-root calculation for most nonlinear systems. Unscented filter technique makes use of a set of sample points to predict mean and covariance. The general QUEST(QUaternion ESTimator) algorithm preserves explicitly the quaternion normalization, whereas extended Kalman filter(EKF) implicitly obeys the constraint. For spacecraft attitude estimation based on quaternion, an approach to computing quaternion means from sampled quaternions with guarantee of the quaternion norm constraint is introduced applying a constrained optimization technique. Finally, the performance of the new approach is demonstrated using a star tracker and rate-gyro measurements.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.342-342
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• 2000

We present 3-axis stabilized spacecraft attitude determination algorithm using the magnetometer. The magnetometer has been used as a reliable, light-weight and inexpensive sensor in attitude determination and reaction wheel momentum dumping system. Recent studies have attempted to use the magnetometer when other attitude sensor, such as star tracker, fails. The differences between the measured and computed the Earth's magnetic field components are spacecraft attitude errors. In this paper, we propose extended Kalman filter(EKF) to determine spacecraft attitude with the magnetometer data and gyro-measured body rates. We develop and simulate this algorithm using MATLAB/SIMULINK. This algorithm can be used as a backup attitude determination system.

• Journal of Advanced Navigation Technology
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• v.4 no.2
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• pp.162-170
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• 2000

Maneuvering targets are difficult for the Kalman filter to track since the target model of tracking filter might not fit the real target trajectory and the statistical characteristics of the target maneuver are unknown in advance. In order to track such a wildly maneuvering target, several schemes had been proposed and improved the tracking performance in some extent. In this paper a Kalman filter-based scheme is proposed for maneuvering target tracking. The proposed scheme estimates the target acceleration input vector directly from the feature of maneuvering target trajectories and updates the simple Kalman tracker by use of the acceleration estimates. Simulation results for various target profiles are analyzed for a comparison of the performances of our proposed scheme with that of conventional trackers.

• Journal of Advanced Navigation Technology
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• v.15 no.6
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• pp.986-993
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• 2011

In this paper we consider the problem of the existing TCAS-II systems that fail to be satisfactory solution to mid-air collisions (MACs) and near mid-air collisions (NMACs or near misses). This is attributed to the fact that the earlier studies on the collision avoidance mainly have focused on determination logic of avoidance direction and vertical speed, reversal of the avoidance direction, multiple aircraft geometry, and availability in certain air spaces. But, the influence of sensor measurement errors on the performance of collision avoidance was not properly taken into account. Here we propose a new TCAS algorithm by using Kalman filter instead of '${\alpha}-{\beta}$' tracker to improve the avoidance performance under the influence of barometric sensor errors due to air-temperature, pressure leaks, static source error correction, etc.