• Journal of Sensor Science and Technology
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• v.25 no.3
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• pp.202-207
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• 2016

Estimation of vertical position is critical in applications of sports science and fall detection and also controls of unmanned aerial vehicles and motor boats. Due to low accuracy of GPS(global positioning system) in the vertical direction, the integration of IMU(inertial measurement unit) with the GPS is not suitable for the vertical position estimation. This paper investigates an IMU-barometer integration for estimation of vertical position (as well as vertical velocity). In particular, a new two-step Kalman/complementary filter is proposed for accurate and efficient estimation using 6-axis IMU and barometer signals. The two-step filter is composed of (i) a Kalman filter that estimates vertical acceleration via tilt orientation of the sensor using the IMU signals and (ii) a complementary filter that estimates vertical position using the barometer signal and the vertical acceleration from the first step. The estimation performance was evaluated against a reference optical motion capture system. In the experimental results, the averaged estimation error of the proposed method was 19.7 cm while that of the raw barometer signal was 43.4 cm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.6
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• pp.492-501
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• 2016

Three-Loop autopilot is generally used for the acceleration control of guided missiles. Because the acceleration command to the three-loop autopilot is given as values at the center of gravity, feedback information of IMU should be obtained at the same position. However, the position of IMU might not be located at the center of gravity due to the sub-system assignment. This paper presents the stability analysis of three-loop autopilot with respect to the arbitrary position of IMU and variation rate of center of gravity. Gain and phase margins are calculated for several trim points for general anti-tank missiles.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.2
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• pp.163-171
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• 2014

Based on the GPS/IMU integration, the car navigation has unstable conditions as well as drastically reduces accuracies in urban region. Nowadays, many cars mounted the camera to record driving states. If the ground coordinates of street furniture are known, the position and attitude of camera can be determined through SPR(Single Photo Resection). Therefore, an estimated position and attitude from SPR can be applied measurements in Kalman filter for updating errors of navigation solutions from GPS/IMU integration. In this study, the GPS/IMU/SPR integration algorithm was developed in loosely coupled modes through extended Kalman filters. Also, in order to analyze performances of GPS/IMU/SPR, simulation tests were conducted in GPS signal reception environments and the GCPs (Ground Control Points) distributions. In fact, the position and attitude gathered from GPS/IMU/SPR integration are more precise than the position and attitude from GPS/IMU integration. When IPs (image points), corresponded to GCPs, were concentrated in the center of image, the position error in the optical axis respectively increased. To understand effects from SPR, we plan to carry additional test on the magnitude of GCP, IP and initial exterior orientation errors.

• Journal of Drive and Control
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• v.13 no.1
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• pp.49-53
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• 2016

The accurate calculation of bucket tip position has a large influence on showing the motion of an excavator on the display device of the excavator and controlling the excavator automatically. It is generally known that Inertial Measurement Unit (IMU) sensors are more accurate than accelerometer-based sensors while the boom, arm or bucket moves because additional forces beyond gravity add additional acceleration to the sensors. To prove the accuracy difference between the two types of sensors, a position recognition system using an accelerometer-based sensor and an IMU sensor is implemented on the excavator. The experimental results show that the system using the IMU sensor significantly reduces the position recognition error while bucket moves and additional force beyond gravity exists.

• Journal of Practical Engineering Education
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• v.10 no.2
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• pp.119-124
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• 2018

Educational equipment that enables the user or device to recognize the indoor position by using the acceleration and angular velocity of the IMU (Inertial Measurement Unit) sensor is introduced. With this educational equipment, various position recognition and tracking algorithms can be learned and creative engineering design works can be realized. The data value of the IMU sensor is transmitted to the MCU (microcontroller unit) through $I^2C$ (Inter-Integrated Circuit), and the indoor position recognition algorithm is applied by processing the data value through the filter and numerical method. It is then designed to use wireless communication to send and receive processed values and to be recognized by the user. As an example using this equipament, the case of "Implementation and recognition of virtual position using computation of moving direction and distance using IMU sensor" is introduced, and various creative engineering design application is discussed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.491-492
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• 2022

본 연구에서는 실내 공간 정보 획득을 위한 IMU/INS 항법장치에 관한 연구를 위한 선행연구를 수행 하였다. 최근의 GPS를 이용한 내비게이션의 경우 보통 5~10m의 위치 오차가 일어나지만 아파트나 대형시설과 같이 실내, 터널, 공장지대 및 산악 지대등 상당한 지역은 GPS의 사각지대 또는 오차 범위를 벗어난 지역으로 존재하고 있다. 따라서 GPS는 실내에서는 사용이 불가능 하므로 다른 방안이 제시되어야 한다. 현재 고속 연산을 위한 고성능 마이크로프로세서의 발전은 센서 분야에 적용되어 저 전력, 고 정밀, 소형의 IMU/INS, ARS/AHRS 센서가 개발되고 있다. 본 연구에서는 IMU(inertial measurement unit)와 INS(Inertial Navigation System)을 이용하여 IMU자체의 자이로 센서와 가속도 센서를 이용한 GPS의 위성신호가 감지되지 않는 지형에서도 속도의 적분값과 회전방향을 이용하여 위치인식이 가능하도록 정보를 계산하여 자기의 위치를 추적하는 방안을 연구하였다.

• Journal of the Korean Geophysical Society
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• v.9 no.4
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• pp.361-369
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• 2006

This study conducted an initialization test to decide dynamic position using AHRS IMU sensor, and derived attitude correction angles of vehicles against time through regression analysis. It was also found that the heading angle was stabilized with variation less than 1°after 60 seconds. Using these angles, this study carried out an experiment on the determination of dynamic position for each system in the open sky and in a semi-open sky. According to the results, in the open sky, DGPS alone systems were excellent in accuracy but poor in data acquisition, so the moving distance was around 12m. In DGPS/IMU combined system, accuracy and data acquisition were satisfactory and the moving distance was around 0.3m. In a semi-open sky, DGPS alone systems were excellent in accuracy in order of DGPS < FIMU < DGPS/IMU according to average and standard errors obtained with exclusion of places where data were not be obtained. The moving distance was the same as that in the open sky. For DGPS, when places where data were not obtainable were divided into Several block and they were compared, the maximum deviation from the trajectory was up to 41.5m in DGPS alone system, but it was less than 2.2m and average and standard errors were significantly improved in the combined system. When the navigation system was applied to surveys and the result was compared with position error 0.2mm under the guideline for digital map, it was possible to work on maps on a scale of up to 1 : 1,000.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.54-61
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• 2012

In this paper, the pose estimation method for the satellite GTB (Ground Test Bed) using vision/MEMS IMU (Inertial Measurement Unit) integrated system is presented. The GTB for verifying a satellite system on the ground is similar to the mobile robot having thrusters and a reaction wheel as actuators and floating on the floor by compressed air. The EKF (Extended Kalman Filter) is also used for fusion of MEMS IMU and vision system that consists of a single camera and infrared LEDs that is ceiling landmarks. The fusion filter generally utilizes the position of feature points from the image as measurement. However, this method can cause position error due to the bias of MEMS IMU when the camera image is not obtained if the bias is not properly estimated through the filter. Therefore, it is proposed that the fusion method which uses the position of feature points and the velocity of the camera determined from optical flow of feature points. It is verified by experiments that the performance of the proposed method is robust to the bias of IMU compared to the method that uses only the position of feature points.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1011-1019
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• 2019

In this paper, we propose a system that can more precisely identify and monitor the position of the tool used in the assembling workplace such as automobile production. The proposed positioning monitoring system is a combination of UWB communication module and MEMS IMU sensor. Since UWB does not need modulation and demodulation function and has low power density, UWB is widely used in indoor positioning field. However, it may cause positioning error due to errors in RF transmission and reception process, which may cause positioning accuracy. Therefore, in this paper, we propose an algorithm that uses IMU as an auxiliary means to compensate for errors that may occur in positioning using only UWB. The tag and anchor of UWB module measure the transmission / reception time by transmitting signals to each other and then estimate the distance between tag and anchor. The MEMS IMU sensor serves to provide positioning calibration information. The tag, which is a mobile node and attached to a moving tool, measures the three-dimensional position of the tool and transfers the coordinate data to the anchor. Thus, it is possible to confirm whether or not the specific tool is properly used according to the prescribed regulations.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.4
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• pp.285-291
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• 2015

In this paper, we propose a classification algorithm for postures of sitting person by using IMU(inertial measurement unit). This algorithm uses PCA(principle component analysis) for decreasing the number of feature vectors to three and SVM(support vector machine) with RBF(radial basis function) kernel for classifying posture types. In order to collect the data, we designed neckband-shaped earphones with IMU, and applied it to three subjects who are healthy adults. Subjects were experimented three sitting postures, which are neutral posture, smartphoning, and writing. As the result, our PCA-SVM algorithm showed 95% confidence while the dimension of the feature vectors was reduced to 25%.