• Journal of Navigation and Port Research
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• v.35 no.10
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• pp.785-791
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• 2011

This paper describes the evaluation and selection of MEMS(Micro-Elect Mechanical System) based inertial sensor to fit to implement the Inertial Measurement Unit(IMU) for a small-sized vessel at sea. At first, the error model and the noise model of the inertial sensors are defined with Euler's equations and then, the inertial sensor evaluation is carried out with Allan Variance techniques and Monte Carlo simulation. As evaluation results for the five sensors, ADIS16405, SAR10Z, SAR100Grade100, LIS344ALH and ADXL103, the combination of gyroscope and accelerometer of ADIS16405 is shown minimum error having around 160 m/s standard deviation of velocity error and around 35 km standard deviation of position error after 600 seconds. Thus, we select the ADIS16405 inertial sensor as a MEMS-based inertial sensor to implement IMU and, the error reducing method is also considered with the search for reference papers.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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• pp.83-90
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• 2003

This paper presents an underwater hybrid navigation system for a semi-autonomous underwater vehicle (SAUV). The navigation system consists of an inertial measurement unit (IMU), and a Doppler velocity log (DVL), accompanied by a magnetic compass. The errors of inertial measurement units increase with time, due to the bias errors of gyros and accelerometers. A navigational system model is derived, to include the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 20. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors, and correct the state equation when the measurements are available. Simulation was performed with the 6-d.o,f equations of motion of SAUV, using a lawn-mowing survey mode. The hybrid underwater navigation system shows good tracking performance, by updating the error covariance and correcting the system's states with the measurement errors from a DVL, a magnetic compass, and a depth sensor. The error of the estimated position still slowly drifts in the horizontal plane, about 3.5m for 500 seconds, which could be eliminated with the help of additional USBL information.

• 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.18 no.4
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• pp.314-320
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• 2012

This paper proposes a new robust motion deblurring filter using the inertial sensor measurements for strapdown image IR applications. With taking the PSF measurement error into account, the motion blurred image is modeled by the linear uncertain state space equation with the noise corrupted measurement matrix and the stochastic parameter uncertainty. This motivates us to solve the motion deblurring problem based on the recently developed robust least squares estimation theory. In order to suppress the ringing effect on the deblurred image, the robust least squares estimator is slightly modified by adoping the ridge-regression concept. Through the computer simulations using the actual IR scenes, it is demonstrated that the proposed algorithm shows superior and reliable motion deblurring performance even in the presence of time-varying motion artifact.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.149-156
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• 2003

This paper presents an underwater hybrid navigation system for a semi-autonomous underwater vehicle (SAUV). The navigation system consists of an inertial measurement unit (IMU), an ultra-short baseline (USBL) acoustic navigation sensor and a doppler velocity log (DVL) accompanying a magnetic compass. The errors of inertial measurement units increase with time due to the bias errors of gyros and accelerometers. A navigational system model is derived to include the error model of the USBL acoustic navigation sensor and the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 25 in the order. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors and correct the state equation when the measurements are available. Simulation was performed with the 6-d.o.f. equations of motion of SAUV in a lawn-mowing survey mode. The hybrid underwater navigation system shows good tracking performance by updating the error covariance and correcting the system's states with the measurement errors from a DVL, a magnetic compass and a depth senor. The error of the estimated position still slowly drifts in horizontal plane about 3.5m for 500 seconds, which could be eliminated with the help of additional USBL information.

• International Journal of Aeronautical and Space Sciences
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• v.6 no.1
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• pp.27-43
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• 2005

An INS(Inertial Navigation System) is composed of a navigation computer and an IMU(Inertial Measurement Unit), and can be applied to estimate a vehicle's state. But the inertial sensors assembled in the IMU are too complicated and expensive to use for the general application purpose. In this study, a new concept of inertial sensor system using magnetic levitation is proposed. The proposed system is expected to replace one single-axis rate or position gyroscope, and one single-axis accelerometer concurrently with a relatively simple structure. A simulation of the proposed system is given to describe the capability of this new concept.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.5
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• pp.522-526
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• 2015

Generally, underwater unmanned vehicle have adopted an inertial navigation system (INS), dead reckoning (DR), acoustic navigation and geophysical navigation techniques as the navigation method because GPS does not work in deep underwater environment. Even if the tactical inertial sensor can provide very detail measurement during long operation time, it is not suitable to use the tactical inertial sensor for small size and low cost UUV because the tactical inertial sensor is expensive and large. One alternative to INS is attitude heading reference system (AHRS) with the micro-machined electro mechanical system (MEMS) inertial sensor because of MEMS inertial sensor's small size and low power requirement. A cost effective and small size attitude heading reference system (AHRS) which incorporates measurements from 3-axis micro-machined electro mechanical system (MEMS) gyroscopes, accelerometers, and 3-axis magnetometers has been developed to provide a complete attitude solution for UUV. The AHRS based MEMS overcome many problems that have inhibited the adoption of inertial system for small UUV such as cost, size and power consumption. Several evaluation experiments were carried out for the validation of the developed AHRS's function and these experiments results are presented. Experiments results prove the fact that the developed MEMS AHRS satisfied the required specification.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3011-3024
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• 2021

Pose estimation of the sensor is important issue in many applications such as robotics, navigation, tracking, and Augmented Reality. This paper proposes visual-inertial integration system appropriate for dynamically moving condition of the sensor. The orientation estimated from Inertial Measurement Unit (IMU) sensor is used to calculate the essential matrix based on the intrinsic parameters of the camera. Using the epipolar geometry, the outliers of the feature point matching are eliminated in the image sequences. The pose of the sensor can be obtained from the feature point matching. The use of IMU sensor can help initially eliminate erroneous point matches in the image of dynamic scene. After the outliers are removed from the feature points, these selected feature points matching relations are used to calculate the precise fundamental matrix. Finally, with the feature point matching relation, the pose of the sensor is estimated. The proposed procedure was implemented and tested, comparing with the existing methods. Experimental results have shown the effectiveness of the technique proposed in this paper.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.9
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• pp.617-625
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• 2022

This study investigates the use of movable calibration target for gyroscopic and accelerometer bias compensation of inertial measurement units for firing lane alignment. Calibration source is detected with the help of vision sensor and its information in fused with other sensors on launcher for error correction. An algorithm is proposed and tested in simulation. It has been shown that it is possible to compensate sensor biases in firing launcher in few seconds by accurately estimating the location of calibration target in inertial frame of reference.