• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.77-81
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• 2006

GPS cannot determine random errors such as multipath and signal cutoff caused by surrounding environment that determines the visibility of satellites and the speed of data creation and transmission is lower than the speed of vehicles, it is difficult to determine accurate dynamic positions. Thus this study purposed to implement a method of deciding the accurate dynamic position of vehicles by combining AHRS (Attitude Heading Reference System) IMU (Initial Measurement Unit) based on low-priced MEMS (Micro Electro Mechanical System) in order to provide the information of attitude, position and speed at a high transmission rate without external help. 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. The roll angle was $y=(A{\times}10^{-6})x^2 -(B{\times}10^{-5})x+Cr{\times}10^{-2}$ and the pitch angle was $y=(A{\times}10^{-6})x^2-(B{\times}10^{-7})x+C{\times}10^{-2}$, each of which was derived from second-degree polynomial regression analysis. It was also found that the heading angle was stabilized with variation less than $1^{\circ}$ after 60 seconds.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.277-283
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• 2014

In this paper, we propose a control system for synchronizing attitude between an Android smartphone and a mobile robot. The control system is comprised of a smartphone and a mobile robot. The smartphone transports its attitude to the mobile robot and receives the attitude of mobile robot through bluetooth communication. Further, the smartphone displays the mobile robot on the screen by using embedded camera, which can be used as a pseudo augmented reality. Comparing the received attitude data from smartphone, the mobile robot measures its attitude by an AHRS(attitude heading reference system) and controls its attitude. Experiments show that the synchronization performance of the proposed system is maintained in the error range of $1^{\circ}$.

• 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.

• Journal of Advanced Navigation Technology
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• v.15 no.2
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• pp.181-188
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• 2011

A small UAVs(Unmaned Aerial Vehicles) have limited by the payload capacity which requires miniaturization of a navigation system. In this paper, the performance of the lightweight and small sized AHRS(Attitude Heading Reference System), which is self-developed, is evaluated at low acceleration environment. The designed AHRS adopts the commercial low-cost MEMS sensors. A quaternion-based attitude calculation method, which eliminates singularity with relatively simple algebra, is used. In an attitude correction algorithm, the Kalman filter is used with accelerometers and magnetometers combined. The fabricated AHRS is also evaluated with reference to a COTS(Commercial Off-The-Shelf) AHRS which reports a number of successful applications to a small UAVs. The test results show that the measurements from the fabricated AHRS provide proper attitude output data with acceptable amount of differences(horizontal axis 0.5$^{\circ}$, vertical axis 1.5$^{\circ}$) in test environment.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.83-89
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• 2016

Internet of Things (IoT) is widely used for biomechanics in sports activities and AHRS(Attitude and Heading Reference System) is a more cost effective solution than conventional high-grade IMUs (Inertial Measurement Units) that only integrate gyroscopes. In this paper, we attach the AHRS to the snowboard to measure the motion data like Air To Fakie, Caballerial and Free Style. In order to reduce the measurement error, we have adopted the sensors equipped with Kalman filtering and also used Euler angle to quaternion conversion to reduce the Gimbal-lock effect. We have tested and evaluated the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it on the basic motions of Snowboarding from the 9-axis trajectory information which is gathered from AHRS sensor. With the result, PCA, ICA have low accuracy, but SVM have good accuracy to use for recognition of basic motions of Snowboarding.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.265-274
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• 2019

The paper describes an application of unscented Kalman filter(UKF) for attitude estimation of an unmanned vehicle(UV), which is equipped with a low-cost attitude heading reference system (AHRS). The roll, pitch and yaw required at the correction stage of the UKF are calculated from the measurements of acceleration and geomagnetic field. The roll and pitch are attributed to the measurement of acceleration, while yaw is calculated from the geomagnetic field measurement. Since the measurement of geomagnetic field is vulnerable to distortion by hard-iron and soft-iron effects, the calculated yaw has more uncertainty than the calculated roll and pitch. To reduce the uncertainty of geomagnetic field measurement, the proposed method estimates bias in the geomagnetic field measurement and compensates for the bias for more accurate calculation of yaw. The proposed method is verified through navigation experiments of a UV in a test pool. The results show that the proposed method yields more accurate attitude estimation; thus, it results more accurate location estimation.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.2
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• pp.137-144
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• 2015

In this paper, we present an inertial sensor-based gait analysis system to measure and analyze lower-limb movements. We developed an integral AHRS(Attitude Heading Reference System) using a combination of rate gyroscope, accelerometer and magnetometer sensor signals. Several AHRS modules mounted on segments of the patient's body provide the quaternions representing the patient segments's orientation in space. And a method is also proposed for calculating three-dimensional inter-segment joint angle which is an important bio-mechanical measure for a variety of applications related to rehabilitation. To evaluate the performance of our AHRS module, the Vicon motion capture system, which offers millimeter resolution of 3D spatial displacements and orientations, is used as a reference. The evaluation resulted in a RMSE(Root Mean Square Error) of 1.08 and 1.72 degree in yaw and pitch angle. In order to evaluate the performance of our the gait analysis system, we compared the joint angle for the hip, knee and ankle with those provided by Vicon system. The result shows that our system will provide an in-depth insight into the effectiveness, appropriate level of care, and feedback of the rehabilitation process by performing real-time limb or gait analysis during the post-stroke recovery.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2511-2520
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• 2011

This paper describes the parameter adjustment method of fuzzy membership function to improve the performance of multi-sensor fusion system using adaptive fuzzy-Kalman filter and cross-validation. The adaptive fuzzy-Kanlman filter has two input parameters, variation of accelerometer measurements and residual error of Kalman filter. The filter estimates system noise R and measurement noise Q, then changes the Kalman gain. To evaluate proposed adaptive fuzzy-Kalman filter, we make the two-axis AHRS(Attitude Heading Reference System) using fusion of an accelerometer and a gyro sensor. Then we verified its performance by comparing to NAV420CA-100 to be used in various fields of airborne, marine and land applications.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.863-868
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• 2015

In this paper, the vibration of a single-wheel mobile robot is minimized by designing a filter. An AHRS (Attitude and heading reference system) sensor is used for measuring the state of the robot. The measured signals are analyzed using the FFT method to investigate the fundamental vibrational frequency with respect to the flywheel's speed of the gimbal system. The IIR notch filter is then designed to suppress the vibration at the identified frequency. After simulating the performance of the designated filter using the measured sensor data through extensive experiments, the filter is actually implemented in a single-wheel mobile robot, GYROBO. Finally, the performance of the designed filter is confirmed by performing the balancing control task of the GYROBO system.