• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.91.4-91
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• 2001

In this paper, a development of the sensor fusion algorithm for a visual control of mobile robot is presented. The output data from the visual sensor include a time-lag due to the image processing computation. The sampling rate of the visual sensor is considerably low so that it should be used with other sensors to control fast motion. The main purpose of this paper is to develop a method which constitutes a sensor fusion system to give the optimal state estimates. The proposed sensor fusion system combines the visual sensor and inertial sensor using a modified Kalman filter. A kind of multi-rate Kalman filter which treats the slow sampling rate ...

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2521-2524
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• 2003

Since inertial sensor errors which increase with time are caused by initial orientation error and sensor errors (accelerometer bias and gyro drift bias), the accuracy of these devices, while still improving, is not adequate for many of today's high-precision, long-duration sea, aircraft, and long-range missile missions. This paper presents a navigation error compensation scheme for Strap-Down Inertial Navigation System (SDINS) using Line-Of-Sight(LOS) vector from star sensor. To be specific, SDINS error model and measurement equation are derived, and Kalman filter is implemented. Simulation results show the bounded-ness of position and attitude errors.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.6
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• pp.30-37
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• 2008

The ARS(Attitude Reference System) calculates an attitude of a vehicle using inertial angular rate sensors and acceleration sensors. The attitude error of ARS increases due to the integration of angular rate sensor output. To reduce the attitude error an acceleration of sensor is used similar to leveling method of INS(Inertial Navigation System). When an acceleration of vehicle is increased, it is difficult to calculate the attitude error using acceleration sensor output. In this paper the estimation method of acceleration due to the attitude error only is proposed. Two methods of the attitude calculation depending on vehicle dynamics and the integration method of these two methods are proposed. To verify its performance the monte carlo simulation is performed and shows that it bounds attitude error of ARS to reasonable level.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.888-893
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• 2005

Since inertial sensor errors which increase with time are caused by initial orientation error and sensor errors(accelerometer bias and gyro drift bias), the accuracy of these devices, while still improving, is not adequate for many of today's high-precision, long-duration sea, aircraft, and long-range flight missions. This paper presents a navigation error compensation scheme for Strap-Down Inertial Navigation System(SDINS) using star tracker. To be specific, SDINS error model and measurement equation are derived, and Kalman filter is implemented. Simulation results show the boundedness of position and attitude errors.

• 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.

• Proceedings of the KIEE Conference
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• 1998.07e
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• pp.1843-1845
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• 1998

Gyroscope is a very important core sensor as a rotation sensor in inertial space, in inertial guidance and navigation system on aeronautics. Plane, vessel and so on for civilian and millitary applications. Research and development of fiber optic gyroscope began in 1976 and focused on improving the gyroscope's sensitivity to rotation. bias performance and reducing noise. We have developed a Interferometric Fiber Optic' Gyroscope using a integrated-optic-circuit (IOC), which is operating with closed-loop electronic circuit. This paper describes the scheme of optical part and electronic part and also test results of this fiber optic gyroscope using a integrated-optic-circuit (IOC). The performance have been achieved as long-term bias drift of $1.73^{\circ}/h$.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.188-197
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• 2021

In military operations, an accurate localization system is required to navigate soldiers to their destinations, even in non-GPS environments. The global positioning system is a commonly used localization method, but it is difficult to maintain the robustness of GPS-based localization against jamming of signals. In addition, GPS-based localization cannot provide important terrain information such as obstacles. With the widespread use of embedded sensors, sensor-based pedestrian tracking schemes have become an attractive option. However, because of noisy sensor readings, pedestrian tracking systems using motion sensors have a major drawback in that errors in the estimated displacement accumulate over time. We present a group-based standalone system that creates terrain maps automatically while also locating soldiers in mountainous terrain. The system estimates landmarks using inertial sensors and utilizes split group information to improve the robustness of map construction. The evaluation shows that our system successfully corrected and combined the drift error of the system localization without infrastructure.

• Physical Therapy Korea
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• v.30 no.2
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• pp.102-109
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• 2023

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model's performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.3
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• pp.46-50
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• 2008

The present status and future prospects of the levitation mass method (LMM), a technique for precision mass and force measurement, are reviewed. In the LMM, the inertial force of a mass levitated using a pneumatic linear bearing is used as the reference force applied to the objects being tested, such as force transducers, materials, or structures. The inertial force of the levitated mass is measured using an optical interferometer. We have modified this technique for dynamic force calibration of impact, oscillation, and step loads. We have also applied the LMM to material testing, providing methods for evaluating material viscoelasticity under an oscillating or impact load, evaluating material friction, evaluating the biomechanics of a human hand, and generating and measuring micro-Newton-level forces.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.3
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• pp.349-360
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• 2018

This paper proposes various methods for constructing a HWIL simulation system including Inertial Navigation System(INS) and Guidance Control Unit(GCU) under the assumption that the INS identifies the initial attitude of an aviation body through its own alignment and that it is a package consisting of an inertial sensor and a navigation computation module. This paper also presents a real-time computing technology and a way to calculate the command of the Flight Motion System(FMS) analogous to the acutal flight environment. The proposed HWIL simulation system is constructed by applying the above-mentioned methods and the results of running a series of simulations confirm high effectiveness and usefulness of the system. Finally, minor error factors that could be acquired only in HWIL simulation Environment are analyzed.