• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.1-15
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• 2021

Purpose: The purpose of this study was to propose a sensor fusion algorithm that integrates vehicle motion sensor(VMS) into the hybrid navigation system. Methods: How to evaluate the navigation performance was comparison test with the hybrid navigation system and the sensor fusion method. Results: The results of this study are as follows. It was found that the effects of the sensor fusion method and α value estimation were significant. Applying these greatly improves the navigation performance. Conclusion: For improving the reliability of navigation system, the sensor fusion method shows that the proposed method improves the navigation performance in a combat vehicle.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.67-73
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• 2023

As interest in underwater structures and ocean exploration increases, many researchers are proposing methods for modeling and controlling various remotely operated vehicles (ROVs). Recently, hybrid systems composed of an autonomous underwater vehicle and an ROV capable of remote control and autonomous navigation are being developed. In this study we introduce a method that models Ariari-aROV, an ROV consisting of five thrusters, and performs navigation. The proposed ROV can be controlled manually and by autonomous navigation when given a target point. An extended Kalman filter is utilized for sensor measurement correction for more precise navigation. The proposed method is verified through a simulation.

• 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 Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.106-112
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• 2006

A new model for the continuous-magnetic interferences has been proposed in this paper to remove external interferes of magnetic field to the dual electric compass. By this removal, the dual electric compass can be used for proving the azimuth angle in an automobile navigation system instead of the rate gyroscope. In the navigation system with a GPS receiver, a DR sensor such as a rate gyroscope is needed to overcome the shielded areas, which is relatively expensive and requires frequent calibrations. However the dual electric compass designed by this research is cheap and provides absolute azimuth angle precisely, which is beneficiary to be used as a DR sensor. The main contribution of this paper is that the long-lasting magnetic interferences have been removed out by using the proposed model, which never be studied before. With a hybrid navigation system using a DR sensor, we demonstrated that dual electric compass is better than a rate gyroscope in terms of both economics and performances.

• Journal of Ocean Engineering and Technology
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• v.19 no.6 s.67
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• pp.78-85
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• 2005

Initial alignment and localization are important topics in inertial navigation systems, since misalignment and initial position error wholly propagate into the navigation systems and deteriorate the performance of the systems. This paper presents the error convergence characteristics of the hybrid navigation system for underwater vehicles initial position, which is based on an inertial measurement unit (IMU) accompanying a range sensor. This paper demonstrates the improvement on the navigational performance oj the hybrid system with the range information, especially focused on the convergence of the estimation of underwater vehicles initial position error. Simulations are performed with experimental data obtained from a rotating ann test with a fish model. The convergence speed and condition of the initial error removal for random initial position errors are examined with Monte Carlo simulation. In addition, numerical simulation is conducted with an AUV model in lawn-mowing survey mode to illustrate the error convergence of the hybrid navigation System for initial position error.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.692-697
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• 2011

There are many researches to develop robots that improve its mobility to adapt in various uneven environments. In the paper, a hybrid mobile robot that can dock with the other robot and transforms between wheeled robot and legged robot is proposed. The hybrid mobile robot platform has docking device with a peg and a cup module. In addition, the robot is possible to walk and drive according to condition of the road. A navigation algorithm of the hybrid mobile robot is proposed to improve the mobility of robots using docking algorithm based on image processing on the broken road and uneven terrain. The proposed method recognizes road condition through PSD sensor attached in front and bottom of the robot and selects an appropriate navigation method according to terrain surface. The proposed docking and navigation methods are verified through experiments using hybrid mobile robots.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.265-270
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• 2006

Recently, inertial sensors like gyros and accelerometers have been quite miniaturized by Micro Electro-Mechanical Systems (MEMS) technology. JAXA is developing a MEM-based GPS/INS hybrid navigation system named Micro-GAIA. The navigation performance of Micro-GAIA was evaluated through off-line analysis by using flight test data. The estimation errors of the roll, pitch, and azimuth were $0.03^{\circ}$, $0.05^{\circ}$, $0.05^{\circ}$ $(1{\sigma})$, respectively. he horizontal position errors after 60-second GPS outages were reduced to 25 m CEP. The attitude errors and position errors are nearly half of ones reported previously[2]. Furthermore, using the adaptive Kalman filters, the robustness against the uncertainty of the measurement noise was improved. Comparing the innovation-based and residual-based adaptive Kalman filters, it was confirmed that the latter is robuster than the former.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.558-565
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• 2011

Various robot platforms have been designed and developed to perform given tasks in a hazardous environment for surveillance, reconnaissance, search and rescue, etc. We considered a terrain-adaptive and transformable hybrid robot platform that is equipped with rapid navigation capability on flat floors and good performance in overcoming stairs or obstacles. The navigation mode transition is determined and implemented by adaptive driving mode control of the mobile robot. In order to maximize the usability of wheel-track hybrid robot platform, we propose a terrain-adaptive and user-friendly remote controller and verify the efficiency and performance through its navigation performance experiments in real and test-bed environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3412-3431
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• 2015

This paper considers the effect of co-channel interference on hybrid satellite-terrestrial relay network. In particular, we investigate the problem of amplify-and-forward (AF) relaying in hybrid satellite-terrestrial link, where the relay is interfered by multiple co-channel interferers. The direct link between satellite and terrestrial destination is not available due to masking by surroundings. The destination node can only receive signals from satellite with the assistance of a relay node situated at ground. The satellite-relay link is assumed to follow the shadowed Rice fading, while the channels of interferer-relay and relay-destination links experience Nakagami-m fading. For the considered AF relaying scheme, we first derive the analytical expression for the moment generating function (MGF) of the output signal-to-interference-plus-noise ratio (SINR). Then, we use the obtained MGF to derive the average symbol error rate (SER) of the considered scenario for M-ary phase shift keying (M-PSK) constellation under these generalized fading channels.