• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.529-539
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• 1999

This paper suggests a new image analysis method and indoor navigation control algorithm of mobile robots using a mono vision system. In order to reduce the positional uncertainty which is generated as the robot travels around the workspace, we propose a new visual landmark recognition algorithm with 2-D graph world model which describes the workspace as only a rough plane figure. The suggested algorithm is implemented to our mobile robot and experimented in a real corridor using extended Kalman filter. The validity and performance of the proposed algorithm was verified by showing that the trajectory deviation error was maintained under 0.075m and the position estimation error was sustained under 0.05m in the resultant trajectory of the navigation.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.114.5-114
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• 2001

In this paper, the model for personal navigation system using low-cost inertial sensors and error compensation method with GPS are proposed. Simulation is accomplished for the performance test. WNS(Walking Navigation System) is a kind of personal navigation using the number of a walk, stride and azimuth. Because the accuracy of these variables determines the navigational performance, computational methods have been investigated. The step is detected using the motion pattern by walking motion, stride is determined by neural network and azimuth is calculated with gyro´s output. The neural network filters off unnecessary motions. However, error compensation method is needed, because the error of navigation information increases with time ...

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.03a
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• pp.219-227
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• 2002

In this paper, we designed neural network predictive PID controller to control sway happened in transfer of trolley for automatic travel control system. We include dynamic character of nonlinear system, and mathematical expression veny simple used neural network. When various establishment location and surrounding disturbance were approved based on mathematical modelling of crane, controller designed to become effective control location error and vibration angle of two control variables that simultaneously can predictive control. Neural network predictive PID controller produced parameter of PID controller using neural network self-tuner. Neural network self-tuner's input used crane's output and neural network predictive output. Neural network self-tuner using error back propagation algorithm. We analyzed control performance comparison through computer simulation when applied disturbance about sway of location and angle in transfer of crane. The results show that the proposed neural network predictive PID controller has better performances than general PID controller, neural network PID controller.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.944-952
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• 2001

Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it is desired to combine INS with external aids such as GPS. However GPS informations have a randomly abrupt jump due to a sudden corruption of the received satellite signals and environment, and moreover GPS can\`t provide navigation solutions. In this paper, smoothing and prediction schemes are proposed for GPS`s jump or unavailable GPS. The smoothing algorithm which is designed as a scalar adaptive filter, smooths abrupt jump. The prediction algorithm which is proved by Schuler error model of INS, estimates INS error in appropriate time. The outputs of proposed algorithm apply stable measurements to GPS aided INS Kalman filter. Simulations show that the proposed algorithm can effectively remove measurement jump and predict INS error.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.56.1-56
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• 2001

Initial attitude error is one of the large error sources in the navigation errors of SDINS. And it is important to decide the initial attitude of SDINS. The method, like a self-alignment or a transfer alignment method, is required to a precise INS. If we do not have a precise INS, we should get large attitude error. After performing the initial alignment, a vehicle has the initial attitude error. Therefore, it results in navigation error due to the initial attitude error. But, if we use position information during flight, we could estimate and compensate a vehicle attitude error. So, we can maintain a precise attitude in spite of existing the initial attitude error. Using the uplinked position information from a land-based radar system, the new algorithm estimates the attitude of the SDINS during flight ...

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.72-78
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• 2007

Determination of the local vertical is not trivial for a moving vehicle and in general will require corrections for the Earth geophysical deflection. The vehicle's local vertical can be estimated by INS integration with initial alignment in SDINS(Strap Down INS) system. In general, the INS has drift error and it cause the performance degradation. In order to compensate the drift error, GPS/INS augmented system is widely used. And in the event that GPS is denied or unavailable, celestial navigation using star tracker can be a backup navigation system especially for the military purpose. In this celestial navigation system, the vehicle's position determination can be achieved using more than two star trackers, and the accuracy of position highly depends on accuracy of local vertical direction. Modern tilt sensors or accelerometers are sensitive to the direction of gravity to arc second(or better) precision. The local gravity provides the direction orthogonal to the geoid and, appropriately corrected, toward the center of the Earth. In this paper the relationship between direction of center of the Earth and actual gravity direction caused by geophysical deflection was analyzed by using precision orbit simulation program embedded the JGM-3 geoid model. And the result was verified and evaluated with mathematical gravity vector model derived from gravitational potential of the Earth. And also for application purpose, the performance variation of pure INS navigation system was analyzed by applying precise gravity model.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.207-214
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• 2020

In this paper, we implement linear-quadratic-Gaussian based vector tracking loop (LQG-VTL) instead of conventional extended Kalman filter based vector tracking loop (EKF-VTL). The LQG-VTL can improve the performance compared to the EKF-VTL by generating optimal control input at a specific performance index. Performance analysis is conducted through two factors, frequency thermal noise and frequency dynamic stress error, which determine total frequency tracking error. We derive the thermal noise and the dynamic stress error formula in the LQG-VTL. From frequency tracking error analysis, we can determine control gain matrix in the LQG controller and show that the frequency tracking error of the LQG-VTL is lower than that of the EKF-VTL in all C/N0 ranges. The simulation results show that the LQG-VTL improves performance by 30% in Doppler tracking, so the LQG-VTL can extend pre-integration time longer and track weaker signals than the EKF-VTL. Therefore, the LQG-VTL algorithm is more robust than the EKF-VTL in weak signal environments.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.10
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• pp.977-987
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• 2012

One of the most significant errors in the pseudo-range measurement performance of GNSSes (Global Navigation Satellite Systems) is their multipath error for high-precision applications. Several schemes to mitigate this error have been studied. Most of them, however, have been focused on the GPS (Global Positioning System) L1 C/A (Coarse/Acquisition) signal that was designed in the 1970s and is still being used for civil navigation. Recently, several modernized signals that were especially conceived to more significantly mitigate multipath errors have been introduced, such as Time Multiplexed and Composite Binary Offset Carrier (TMBOC and CBOC, respectively) signals. Despite this advantage, however, a problem remains with the use of TMBOC and CBOC modulations: the ambiguity of BOC (Binary Offset Carrier)-modulated signal tracking. In this paper, a novel unambiguous multipath error mitigation scheme for these modernized signals is proposed. The proposed scheme has the same complexity as HRCs (High Resolution Correlators) but with low ambiguity. The simulation results showed that the proposed scheme outperformed or performed at par with the HRC in terms of their multipath error envelopes and running averages in the static and statistical channel models. The ranging error derived by the mean multipath error of the proposed scheme was below 1.8 meters in an urban area in the statistical channel model.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.2
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• pp.133-139
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• 2007

An unmanned ground vehicle can perform its mission automatically without human control in unknown environment. To move up to a destination in various surrounding situation, navigational information is indispensible. In order to be adopted for an unmanned vehicle, the navigation box is small, light weight and low power consumption. This paper suggests navigation system using a low grade MEMS IMU for supplying position, velocity, and attitude of an unmanned ground vehicle. This system consists of low cost and light weight MEMS sensors and a GPS receiver to meet unmanned vehicle requirements. The sensors are basically integrated by loosely coupled method using Kalman filter and internal algorithms are divided into initial alignment, sensor error compensation, and complex navigation algorithm. The performance of the designed navigation system has been analyzed by real time field test and compared to commercial tactical grade GPS/INS system.