• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.6
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• pp.611-616
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• 2008

When extracting geo-referenced 3D data from cameras mounted on Mobile Mapping Systems, one of important properties for accuracy of extracted data is the alignment of the relative translation(lever-arm) and rotation(bore-sight) between the coordinate systems of Inertial Measurement Unit(IMU)/Ground Positioning System(GPS) and cameras. Since the conventional method calculates absolute camera orientation using ground control points (GCP), the alignment is determined in one Coordinated System (GPS Coordinated System). It basically require GCP. We proposed a mathematical model for the alignment using the initially uncoupled data of cameras and IMU/GPS without GCPs.

• International Journal of Ocean System Engineering
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• v.2 no.4
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• pp.223-232
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• 2012

For the accurate estimation of the position and orientation of a UUV (unmanned underwater vehicle), a low-cost AHRS (attitude heading reference system) was developed using a low-cost IMU (inertial measurement unit) sensor which provides information on the 3D acceleration, 3D turning rate and 3D earth-magnetic field data in the object coordinate system. The main hardware system is composed of an IMU sensor (ADIS16405) and TMS320F28335, which is coded with an extended kalman filter algorithm with a 50-Hz sampling frequency. Through an experimental gimbal device, good estimation performance for the pitch, roll, and yaw angles of the developed AHRS was verified by comparing to those of a commercial AHRS called the MTi system. The experimental results are here presented and analyzed.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.469-475
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• 2015

This paper describes about the method for designing an extended Kalman filter to accurately measure the position of the spatial-phase system using a semiconductor sensor. Spatial position is expressed by the correlation of the rotated coordinate system attached to the body from the inertia coordinate system (a fixed coordinate system). To express the attitude, quaternion was adapted as a state variable, Then, the state changes were estimated from the input value which was measured in the gyro sensor. The observed data is the value obtained from the acceleration sensor. By matching between the measured value in the acceleration sensor and the predicted calculation value, the best variable was obtained. To increase the accuracy of estimation, designation of the extended Kalman filter was performed, which showed excellent ability to adjust the estimation period relative to the sensor property. As a result, when a three-axis gyro sensor and a three-axis acceleration sensor were adapted in the estimator, the RMS(Root Mean Square) estimation error in simulation was retained less than 1.7[$^{\circ}$], and the estimator displayed good property on the prediction of the state in 100 ms measurement period.

• International Journal of Ocean System Engineering
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• v.1 no.3
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• pp.165-170
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• 2011

In this paper, for the accurate estimation of the position and orientation of the UUV (unmanned underwater vehicle), an AHRS (Attitude Heading Reference System) was developed using the IMU (inertial measurement unit) sensor which provides information on acceleration and orientation in the object coordinate and the initial alignment algorithm and the E-KF (extended Kalman Filter). The initial position and orientation of the UUV are estimated using the initial alignment algorithm with 3-axis acceleration and geomagnetic information of the IMU sensor. The position and orientation of the UUV are estimated using the AHRS composed of 3-axis acceleration, velocity, and geomagnetic information and the E-KF. For the performance test of the orientation estimation of the AHRS, a testbed using IMU sensor(ADIS16405) and DSP28335 coded with an E-KF algorithm was developed and its performance was verified through tests.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.1
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• pp.77-88
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• 2015

The dynamics of relative motion in a perturbed orbital environment are exploited based on Gauss' and Cowell's variational equations. The inertial coordinate frame and relative coordinate frame (Hill frame) are used, and a linear high fidelity model is developed to describe the relative motion. This model takes into account the primary gravitational and atmospheric drag perturbations. Then, this model is used in the design of a navigation, guidance, and control system of a chaser vehicle to approach towards and to depart from a target vehicle in proximity operations. Relative navigation uses an extended Kalman filter based on this relative model to estimate the relative position/velocity of the chaser vehicle with respect to the target vehicle. This filter uses the range and angle measurements of the target relative to the chaser from a simulated LIDAR system. The corresponding measurement models, process noise matrix, and other filter parameters are provided. Numerical simulations are performed to assess the precision of this model with respect to the full nonlinear model. The analyses include the navigation errors and trajectory dispersions.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.2
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• pp.71-81
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• 2022

In order for electro-optical tracking system (EOTS) to have accurate target coordinate, accurate navigation results are required. If an integrated navigation system is configured using an inertial measurement unit (IMU) of EOTS and the vehicle's navigation results, navigation results with high rate can be obtained. Due to the time-delay of the navigation results of the vehicle in the EOTS and scale factor errors of the EOTS IMU in high-speed and high dynamic operation of the vehicle, it is much more difficult to have accurate navigation results. In this paper, an integrated navigation system of EOTS which compensates time-delay and scale factor error is proposed. The proposed integrated navigation system consists of vehicle's navigation system which provides time-delayed navigation results, an EOTS IMU, an inertial navigation system (INS), an augmented Kalman filter and integration Kalman filter. The augmented Kalman filter outputs navigation results, in which the time-delay of the vehicle's navigation results is compensated. The integration Kalman filter estimates position, velocity, attitude error of the EOTS INS and accelerometer bias, accelerometer scale factor error, gyro bias and gyro scale factor error from the difference between the output of the augmented Kalman filter and the navigation result of the EOTS INS. In order to check performance of the proposed integrated navigation system, simulations for output data of a measurement generator and land vehicle experiments were performed. The performance evaluation results show that the proposed integrated navigation system provides more accurate navigation results.

• Journal of the Korean Society of Industry Convergence
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• v.13 no.2
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• pp.85-92
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• 2010

This study shows the path generation algorithm for an UGV (Unmanned Ground Vehicle). The developed UGV frame which has a 4-wheel driven mechanism and diesel source is applied. Proposed vehicle system in this research is aimed to military purpose. To achieve the unmanned autonomous driving, following two main issues are considered. First, behavior module for positioning and posture of vehicle system and second, cognition module to receive the information from environment are proposed and verified. To do this, rover which can acquire the positioning information from earth coordinate and IMU (Inertial Measurement Unit) which can measure the posture are combined to design the path planning algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1601-1608
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• 2000

This paper is concerned with the forced flexural vibration analysis of hard disk drive (HDD) spindle systems with multiple thin disks, supported by two ball bearings based on the finite element model. This is the extension of the previous work which analytically modeled every system component taking into account its structural flexibility and also the centrifugal stiffening effect especially for the disks. Among the end results, the forced time response is expectedly useful for the vibration control of the spindle itself or the position servo control of the magnetic head. On the other hand, the steady state responses such as the frequency response function and the unbalance response are useful for system identification. Futhermore, through a coordinate transformation the equations of motion is also derived with respect to the inertial frame for convenient analyses of certain classes.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.8
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• pp.698-705
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• 2002

The redundant sensor configuration problem of inertial navigation system(INS) is considered and analyzed the navigation and fault detection performance according to various sensor configurations. We considered various kinds of redundant sensor configurations for symmetric, cone, and orthogonal configurations and compare the navigation and fault detection performance for the configurations. We show that the navigation and fault detection performance is not affected by the coordinate change for a fixed configuration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2365-2370
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• 2017

In this paper, we propose a motion and position recognition technique considering an experiential VR environment. Motion recognition attaches a plurality of AHRS devices to a body part and defines a coordinate system based on this. Based on the 9 axis motion information measured from each AHRS device, the user's motion is recognized and the motion angle is corrected by extracting the joint angle between the body segments. The location recognition extracts the walking information from the inertial sensor of the AHRS device, recognizes the relative position, and corrects the cumulative error using the BLE fingerprint. To realize the proposed motion and position recognition technique, AHRS-based position recognition and joint angle extraction test were performed. The average error of the position recognition test was 0.25m and the average error of the joint angle extraction test was $3.2^{\circ}$.