• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.6
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• pp.677-683
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• 2024

Landing a helicopter on a moving ship requires accounting for the ship's attitude. However, it is not only challenging to visually assess the ship's attitude, but it also creates illusions for the pilot, increasing the risk of accidents. In this study, we propose an image-based ship attitude estimation method to assist helicopter landings. The proposed method enhances landing safety by predicting the ship's heave, pitch, and roll using only helicopter-mounted optical devices and pre-trained deep learning models, without requiring communication with the ship. To implement this approach, we generated a dataset by simulating a virtual sea environment and ship motion. Using this data, we trained deep learning models to predict the ship's attitude based solely on images. Experimental results confirm the feasibility of the proposed method, with VGG-16 demonstrating particularly effective attitude prediction under simulated conditions.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.162-165
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• 1995

For an accurate on-line measurement of the ship's attitude the paper develops an intelligent sensing system which uses one servo-type accelerometer and two servo-type inclinometers appropriately located on the ship. By considering the dynamics of the servo-controlled rigid pendulums of the inclinometers, linear equations for the rolling and pitching of the ship are derived separately from each other. Moreover, one accelerometer is used for extracting the heaving signal. Through the introduction of linear dynamic models and the linear observation equations for the heaving, rolling and pitching, the on-line measurement of the three signals can be reduced to the state estimation of the linear dynamic systems. A bank of Kalman filters is adaptively used to achieve the on-line accurate state estimation and to overcome changes in parameters in the linear dynamic models.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.178-184
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• 2002

This paper deals with the transfer alignment problem of SDINS(StrapDown Inertial Navigation System) subjected to roll and pitch motions of the ship. In order to reduce alignment errors induced by ship body flexure, a linearized error model for the velocity and attitude matching transfer alignment system is first derived by linearizing the nonlinear measurement equation with respect to the dominant y axis component and defining the flexure state of random constant type. And then a robust state estimation scheme is introduced to account for modeling uncertainty of the flexure. By interpreting the simulation results and comparing with the velocity and DCM(Direction Cosine Matrix) partial matching method, it is shown that the proposed method is effective enough to improve the azimuth alignment performance.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.238-243
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• 1997

A new transfer alignment algorithm using the velocity and the quaternion partial matching methods is proposed to reduce the effect of a ship's Y-axis flexure on the performance of azimuth error estimation of Kalman filter. The simulation results show that it can significantly reduce the effect of Y-axis flexure on error estimation by the transfer alignment algorithm. As its results, azimuth transfer alignment error is reached up to 3 mrad under proper roll and pitch attitude motion of the ship.

• Journal of Ocean Engineering and Technology
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• v.11 no.3
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• pp.170-179
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• 1997

This paper presents a hybrid navigation system for AUV to locate its position precisely in rough sea. The tracking system is composed of various sensors such as an inclinometer, a tri-axis magnetometer, a flow meter, and a super short baseline(SSBL) acoustic position tracking system. Due to the inaccuracy of the attitude sensors, the heading sensor and the flowmeter, the predicted position slowly drifts and the estimation error of position becomes larger. On the other hand, the measured position is liable to change abruptly due to the corrupted data of the SSBL system in the case of low signal to noise ratio or large ship motions. By introducing a sensor fusion technique with the position data of the SSBL system and those of the attitude heading flowmeter reference system (AHFRS), the hybrid navigation system updates the three-dimensional position robustly. A Kalman filter algorithm is derived on the basis of the error models for the flowmeter dynamics with the use of the external measurement from the SSBL. A failure detection algorithm decides the confidence degree of external measurement signals by using a fuzzy inference. Simulation is included to demonstrate the validity of the hybrid navigation system.

• Journal of Mechanical Science and Technology
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• v.19 no.6
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• pp.1253-1267
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• 2005

Misalignment can be an important problem in the integration of GPS/INS. Observability analysis of the alignment errors in the integration of low-grade inertial sensors and multi-antenna GPS is presented in this paper. A control-theoretic approach is adopted to study the observability of time-varying error dynamics models. The relationship between vehicle motions and the observability of the errors in the lever arm and relative attitude between GPS antenna array and IMU is given. It is shown that alignment errors can be made observable through maneuvering. The change of acceleration makes the components of the relative attitude error that are orthogonal to the direction of the acceleration change observable. The change of angular velocity makes the components of the lever arm error that are orthogonal to the direction of the angular velocity observable. The motion of constant angular velocity has no influence on the estimation of the lever arm.