• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.3
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• pp.349-360
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• 2018

This paper proposes various methods for constructing a HWIL simulation system including Inertial Navigation System(INS) and Guidance Control Unit(GCU) under the assumption that the INS identifies the initial attitude of an aviation body through its own alignment and that it is a package consisting of an inertial sensor and a navigation computation module. This paper also presents a real-time computing technology and a way to calculate the command of the Flight Motion System(FMS) analogous to the acutal flight environment. The proposed HWIL simulation system is constructed by applying the above-mentioned methods and the results of running a series of simulations confirm high effectiveness and usefulness of the system. Finally, minor error factors that could be acquired only in HWIL simulation Environment are analyzed.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1144-1148
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• 1996

This paper presents WMR modelling and path tracking algorithm using Inertial Navigation System. The error models of gyroscope and accelerometers in INS are derived by Gauss-Newton method which is nonlinear regression model. Then, to test availability of error model, we pursue the fitness diagnosis about probability characteristic for real data and estimated data. Performance of inertial sensor with error model and Kalman filter is pursued by comparing with one without them. The computer simulation shows that position error remarkably decrease when error compensation is applied.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.495-496
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• 2008

• International Journal of Aeronautical and Space Sciences
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• v.1 no.1
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• pp.81-91
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• 2000

The mechanization of navigation equation is depending on the designer according to the orientation vector relating the body frame to a chosen to inertial and navigation frames for its purposes. This paper considers the appropriate Earth Fixed frame for short range vehicle and develops a mechanization and algorithm for Strapdown Inertial Navigation System(SDINS). This mechanization consists of two parts : translational mechanization and rotational mechanization{attitude determination). The accuracy, availability and performance of this SDINS mechanization are verified on the simulation and the numerical method for integration attitude propagation is compared with a well-known method in a precession motion.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.1
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• pp.29-34
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• 2022

In this paper, we analyze the Non-Holonomic Constraint (NHC) satisfaction of Inertial Navigation System (INS) for vehicular navigation according to Inertial Measurement Unit (IMU) location. In INS-based vehicle navigation, NHC information is widely used to improve INS performance. That is, the error of the INS can be compensated under the condition that the velocity in the body coordinate system of the vehicle occurs only in the forward direction. In this case, the condition that the vehicle's wheels do not slip and the vehicle rotates with the center of the IMU must be satisfied. However, the rotation of the vehicle is rotated by the steering wheel which is controlled based on the Ackermann geometry, where the center of rotation of the vehicle exists outside the vehicle. Due to this, a phenomenon occurs that the NHC is not satisfied depending on the mounting position of the IMU. In this paper, we analyze this problem based on Ackermann geometry and prove the analysis result based on simulation.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.3
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• pp.626-637
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• 2014

On ship, especially on large ship, the flexure deformation between Master (M)/Slave (S) Inertial Navigation System (INS) is a key factor which determines the accuracy of the integrated system of M/S INS. In engineering this flexure deformation will be increased with the added ship size. In the M/S INS integrated system, the attitude error between MINS and SINS cannot really reflect the misalignment angle change of SINS due to the flexure deformation. At the same time, the flexure deformation will bring the change of the lever arm size, which further induces the uncertainty of lever arm velocity, resulting in the velocity matching error. To solve this problem, a $H_{\infty}$ algorithm is proposed, in which the attitude and velocity matching error caused by deformation is considered as measurement noise with limited energy, and measurement noise will be restrained by the robustness of $H_{\infty}$ filter. Based on the classical "attitude plus velocity" matching method, the progress of M/S INS information fusion is simulated and compared by using three kinds of schemes, which are known and unknown flexure deformation with standard Kalman filter, and unknown flexure deformation with $H_{\infty}$ filter, respectively. Simulation results indicate that $H_{\infty}$ filter can effectively improve the accuracy of information fusion when flexure deformation is unknown but non-ignorable.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1117-1124
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• 2010

This paper presents a foot movement tracking system using ultrasonic sensors and inertial sensors, where the position and velocity of foot are computed using inertial sensors and ultrasonic sensors mounted on a shoe. A foot movement can be estimated using an inertial navigation algorithm only; however, the error tends to increase due to biases of gyroscopes and accelerometers. To reduce the error, a localization system using ultrasonic sensors is additionally used. In the localization system using ultrasonic sensors, the position is continuously calculated in the absolute coordinate. An indirect Kalman filter is used to combine inertial sensors and ultrasonic sensors. Through experiments, it is shown that the proposed system can track a foot movement.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.4
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• pp.435-442
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• 2016

For inertial navigation systems, initial information such as position, velocity and attitude is required for navigation. Self-alignment is the process to determine initial attitude on stationary condition using inertial measurements such as accelerations and angular rates. The accuracy of self-alignment is determined by inertial sensor error. As soon as an inertial navigation system is powered on, the temperature of accelerometer rises rapidly until temperature stabilization. It causes acceleration error which is called temperature stabilizing error of accelerometer. Therefore, temperature stabilizing error degrades the alignment accuracy and also increases alignment time. This paper suggests a method to calculate azimuthal attitude using curve fitting of horizontal control angular rate in fixed-gain self-alignment. It is verified by simulation and experiment that the accuracy is improved and the alignment time is reduced using the proposed method under existence of the temperature stabilizing error.