• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.637-648
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• 2007

This paper presents a sensor fusion method based on indirect Kalman filter(IKF) for error compensation of low-cost inertial sensors and its application to the determination of attitude and position of small flying robots. First, the analysis of the measurement error characteristics to zero input is performed, focusing on the bias due to the temperature variation, to derive a simple nonlinear bias model of low-cost inertial sensors. Moreover, from the experimental results that the coefficients of this bias model possess non-deterministic (stochastic) uncertainties, the bias of low-cost inertial sensors is characterized as consisting of both deterministic and stochastic bias terms. Then, IKF is derived to improve long term stability dominated by the stochastic bias error, fusing low-cost inertial sensor measurements compensated by the deterministic bias model with non-inertial sensor measurement. In addition, in case of using intermittent non-inertial sensor measurements due to the unreliable data link, the upper and lower bounds of the state estimation error covariance matrix of discrete-time IKF are analyzed by solving stochastic algebraic Riccati equation and it is shown that they are dependant on the throughput of the data link and sampling period. To evaluate the performance of proposed method, experimental results of IKF for the attitude determination of a small flying robot are presented in comparison with that of extended Kaman filter which compensates only deterministic bias error model.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.1037-1043
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• 2004

In this paper, a new fault detection and isolation algorithm fur inertial sensor system is proposed. To identify the inertial sensor fault, single antenna GPS receiver is used as an effective redundancy source. To use GPS receiver as redundancy for the inertial sensors, the algorithm to estimate the attitude and acceleration using single antenna GPS receiver is adopted. By using Doppler shift of carrier phase signal and kinetic characteristics of aircraft, attitude information of aircraft can be obtained at the coordinated flight condition. Based on this idea, fault diagnosis algorithm for inertial sensors using single antenna GPS based attitude is proposed. For more effective FDI, decision variables considering the aircraft maneuver are proposed. The effectiveness of the proposed algorithm is verified through the numerical simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.11
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• pp.2048-2057
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• 2010

Is this paper, a pedestrian inertial navigation system with vision is proposed. The navigation system using inertial sensors has problems that it is difficult to determine the initial position and the position error increases over time. To solve these problems, a vision system in addition to an inertial navigation system is used, where a camera is attached to a pedestrian. Landmarks are installed to known positions so that the position and orientation of a camera can be computed once a camera views the landmark. Using this position information, estimation errors in the inertial navigation system is compensated.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.1
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• pp.101-108
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• 1998

This paper concerns the navigation algorithm of motion reference unit (MRU) for autonomous underwater vehicle (AUV) We apply the strapdown navigation system using middle level inertial sensors. But, because the MRU consists of inertial sensors, the values of AUV motion calculated by navigation computer are increased by drift property of inertial sensors. Therefore, we propose the attitude algorithm using switching method according to the motion of AUV From this algorithm, the drift terms are eliminated effectively for roll and pitch. But, another device is required for yaw angle.

• Current Research on Agriculture and Life Sciences
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• v.32 no.2
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• pp.79-84
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• 2014

The precision aerial application of agricultural unmanned helicopters has become a new paradigm for small farms with orchards, paddy, and upland fields. The needs of agricultural applications require easy and affordable control systems. Recent developments of MEMS technology based on inertial sensors and high speed DSP have enabled the fabrication of low-cost attitude system. Therefore, this study evaluates inertial MEMS sensors for estimating the attitude of an agricultural unmanned helicopter. The accuracies and errors of gyro and acceleration sensors were verified using a pendulum system. The true motion values were calculated using a theoretical estimation and absolute encoder measurement of the pendulum, and then the sensor output was compared with reference values. When comparing the sensor measurements and true values, the errors were determined to be 4.32~5.72%, 3.53~6.74%, and 3.91~4.16% for the gyro rate and x-, z- accelerations, respectively. Thus, the measurement results confirmed that the inertial sensors are effective for establishing an attitude and heading reference system (AHRES). The sensors would be constructed in gimbals for the estimating and proving attitude measurements in the following paper.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.1
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• pp.1-9
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• 2004

Inertial navigation system with hardware redundancy must use FDI(Fault Detection and Isolation) method to remove the influence of faulty sensors. Until now, several FDI methods such as PSA(Parity Space Approach), GLT(Generalized Likelihood ratio Test) and OPT(Optimal Parity vector Test) method are generally used. However, because these FDI methods only consider the situation that the system has one faulty sensor, these methods cannot be directly adapted for the system with two faulty sensors. To solve this problem, in this paper, PSA method is analyzed and based on this result, new FDI method called EPSA is proposed to consider a detection and an isolation of two faulty sensors in inertial navigation system.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.4
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• pp.377-386
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• 2015

Joint force/torque estimation by inverse dynamics is a traditional tool in biomechanical studies. Conventionally for this, kinematic data of human body is obtained by motion capture cameras, of which the bulkiness and occlusion problem make it hard to capture a broad range of movement. As an alternative, inertial motion sensing using cheap and small inertial sensors has been studied recently. In this research, the performance of inertial motion sensing especially to calculate inverse dynamics is studied. Kinematic data from inertial motion sensors is used to calculate ground reaction force (GRF), which is compared to the force plate readings (ground truth) and additionally to the estimation result from optical method. The GRF estimation result showed high correlation and low normalized RMSE(R=0.93, normalized RMSE<0.02 of body weight), which performed even better than conventional optical method. This result guarantees enough accuracy of inertial motion sensing to be used in inverse dynamics analysis.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.613-620
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• 2013

A new remote control algorithm for a mobile robot is proposed, where a remote controller consists of a camera and inertial sensors. Initially the relative position and orientation of a robot is estimated by capturing four circle landmarks on the plate of the robot. When the remote controller moves to point to the destination, the camera pointing trajectory is estimated using an inertial navigation algorithm. The destination is transmitted wirelessly to the robot and then the robot is controlled to move to the destination. A quick movement of the remote controller is possible since the destination is estimated using inertial sensors. Also unlike the vision only control, the robot can be out of camera's range of view.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.102-106
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• 2013

A cooperative navigation method for cooperative flight of UAVs is proposed. The commonly used navigation method for UAVs is based on GNSS measurements. However, when it is not available by jamming or other causes, an alternative method is needed. In this paper, it is shown that UAVs equipped with inertial sensors, passive sensor and wireless communication link can perform accurate navigation through sharing information with each other. Firstly, the appropriate roles for sensors and wireless communication link are assigned. Secondly, a filter to perform navigation cooperative is constructed. Finally, the boundedness of estimation error of the filter under small initial estimation error is analyzed. The simulation results show that the proposed method can reduce navigation errors effectively.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.118-126
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• 2011

This study proposes a human body orientation tracking system by inertial and earth magnetic sensors. These sensors were fused by indirect Kalman filter. The proposed tracking system was configured and the filter was implemented. The tracking performance was evaluated with static and dynamic tests. In static test, the sensor was fixed on the floor while its static characteristics was analyzed. In dynamic test, the sensor was held and moved manually for 30 seconds. The dynamic test included x, y, z axis rotations, and elbow flection/extension motions that mimic drinking. For these dynamic motions, the tracking angle error was under $4.1^{\circ}$ on average. The proposed tracking method is expected to be useful for various human body motion analysis.