• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.270-270
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• 2019

Although the Inertial Measurement Unit is applied to a variety of applications such as ships, submarines, and aircrafts, it is mainly used in the attitude measurement area. But since such equipment is expensive, it has been used only in special fields. In this study, the ship's seaworthiness is verified by measuring the speed, direction, gravity, and acceleration of the ship in real time using a low-cost Inertial Measurement Unit. A research method for estimating fIuid force coefficients was devised. Therefore, this study measured ship motion factors at sea, processed and analyzed the measured data, and evaluated the overall safety of the ship and estimated the resistance and steering performance of the ship.

• 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 Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1167-1175
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• 2010

This paper presents the use of the inertial measurement unit information and the infrared sector information for getting the position of an object. Travel distance is usually calculated from the double integration of the accelerometer output with respect to time; however, the accumulated errors due to the drift are inevitable. The orientation change of the accelerometer also causes error because the gravity is added to the measured acceleration. Unless three axis orientations are completely identified, the accelerometer alone does not provide correct acceleration for estimating the travel distance. We propose a way of minimizing the error due to the change of the orientation. In order to reduce the accumulated error, the infrared sector information is fused with the inertial measurement unit information. Infrared sector information has highly deterministic characteristics, different from RFID. By putting several infrared emitters on the ceiling, the floor is divided into many different sectors and each sector is set to have a unique identification. Infrared light based sector information tells the sector the object is in, but the size of the uncertainty is too large if only the sector information is used. This paper presents an algorithm which combines both the inertial measurement unit information and the sector information so that the size of the uncertainty becomes smaller. It also introduces a framework which can be used with other types of the artificial landmarks. The characteristics of the developed infrared light based sector and the proposed algorithm are verified from the experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2945-2965
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• 2023

The laser inertial measurement unit is a precision device widely used in rocket navigation system and other equipment, and its quality is directly related to navigation accuracy. In the quality evaluation of laser inertial measurement unit, there is inevitably uncertainty in the index input information. First, the input numerical information is in interval form. Second, the index input grade and the quality evaluation result grade are given according to different national standards. So, it is a key step to transform the interval information input by the index into the data form consistent with the evaluation result grade. In the case of uncertain input, this paper puts forward a method based on probability distribution to solve the problem of asymmetry between the reference grade given by the index and the evaluation result grade when evaluating the quality of laser inertial measurement unit. By mapping the numerical relationship between the designated reference level and the evaluation reference level of the index information under different distributions, the index evidence symmetrical with the evaluation reference level is given. After the uncertain input information is transformed into evidence of interval degree distribution by this method, the information fusion of interval degree distribution evidence is carried out by interval evidential reasoning algorithm, and the evaluation result is obtained by projection covariance matrix adaptive evolution strategy optimization. Taking a five-meter redundant laser inertial measurement unit as an example, the applicability and effectiveness of this method are verified.

• Journal of Ocean Engineering and Technology
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• v.11 no.1
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• pp.95-105
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• 1997

This paper presents a Inertial Measuring Unit(IMU) for motion measurement of an AUV. The IMU is composed of three parts: inertial sensors with three servo accelerometers and three rate gyros, an analog/digital interface board, and a signal processing board with TMS320C31 DSP processor. The IMU is a class of strap-down inwetial navigation system does not applicable directly to the navigation system in consequence of the AUV and integrated sensors for an integrated navigation system of the AUV. Fast calculstion of direction cosine matrix for the coordinate transformation body to reference is obtained through the DSP processor. A switching algotrithm is used to lessen the low frequency drift effect of the gyros in the vertical plane with use of low pass filtering of the signal of the accelerometers.

• 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 Ocean Engineering and Technology
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• v.19 no.5 s.66
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• pp.71-77
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• 2005

This paper presents an integrated underwater navigational algorithm for unmanned underwater vehicles, using additional two-range transducers. This paper proposes a measurement model, using two range measurements, to improve the performance of an IMU-DVL (inertial measurement unit - Doppler velocity log) navigation system for long-time operation of underwater vehicles, excluding DVL measurement. Extended Kalman filter was adopted to propagate the error covariance, to update the measurement errors, and to correct the state equation when the external measurements are available. Simulation was conducted with the 6-d.o.f nonlinear numerical model of an AUV in lawn-mowing survey mode, at current flaw, where the velocity information is unavailable. Simulations illustrate the effectiveness of the integrated navigation system, assisted by the additional range measurements without DVL sensing.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.3
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• pp.234-239
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• 2010

The utilization of micro-electro-mechanical system (MEMS) gyros and accelerometers in low-level inertial measurement unit (IMU) influences cost effectiveness in a positive way under the condition that device error characteristics are fully calibrated. The conventional calibration process utilizes a rate table; however, this paper proposes a new method for achieving reference calibration data from the natural motion of pendulum to which the IMU undergoing calibration is attached. This concept was validated with experimental data. The pendulum angle measurements correlate extremely well with the solutions acquired from the pendulum equation of motion. The calibration data were computed using the regression method. The whole process was validated by comparing the measurement from the 6 sensor components with the measurements reconstructed using the identified calibration data.

• 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.

• Aerospace Engineering and Technology
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• v.9 no.1
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• pp.50-59
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• 2010

In this paper, the alignment and navigation results by INGU(Inertial Navigation and Guidance Unit) onboard software and by Inertial Explorer which is a post-processing software specialized for IMU(Inertial Measurement Unit) are compared for identification of inertial sensor error models and estimation of alignment and navigation errors for KSLV-I INGU. For verification of the IMU error estimated by Kalman Filter of Inertial Explorer, the covariance parameters of inertial sensor error model state are identified by using stochastic error model of inertial sensors estimated by Allan variance and the alignment and navigation test with static condition and the land navigation test with dynamic condition are carried out. The validity of inertial sensor model for KSLV-I INGU is verified by comparison the alignment and navigation results of INGU on-board software and Inertial Explorer.