• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.941-946
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• 2011

The Loran-C, a radio navigation system based on TDOA measurements is enhanced to eLoran using TOA measurements instead of TDOA measurements. Many error factors such as PF, SF, ASF, clock errors and unknown biases are included in eLoran TOA measurements. Because these error factors can cause failure in eLoran navigation algorithm, these errors must be compensated for high accuracy eLoran navigation results. Compensation of ASF and unknown biases are difficult to calculate, while the others such as PF and SF are relatively easy to eliminate. In order to compensate all errors in eLoran TOA measurements, a simple GPS aided bias compensation method is suggested in this paper. This method calculates the bias as the difference of TOA measurement and the range between eLoran transmitters and the receiver whose position is determined using GPS. The real data measured in Europe are used for verification of suggested method and navigation algorithm.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.12-18
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• 2012

This paper presents a computation method of LOS(Line Of Sight) angle using IMU(Inertial Measurement Unit) for an antenna on aerial vehicle to point to a stationary satellite. In the overall system, the antenna is located at the front of the vehicle, and an IMU is introduced to account for body flexure dynamic. And using the differences between the position and velocity of the IMU based navigation and those of GPS/INS at the vehicle center. Kalman filter is designed to suppress Strapdown INS drift errors.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.468-473
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• 2014

This paper describes the in-flight alignment of SDINS (Strapdown Inertial Navigation Systems) using an EKF (Extended Kalman Filter) and a UKF (Unscented Kalam Filter), which allow large initial attitude error uncertainty. Regardless of the inertial sensors, there are nonlinear error dynamics of SDINS in cases of large initial attitude errors. A UKF that is one of the nonlinear filtering approaches for IFA (In-Flight Alignment) are used to estimate the attitude errors. Even though the EKF linearized model makes velocity errors when predicting incorrectly in case of large attitude errors, a UKF can represent correctly the velocity errors variations of attitude errors with nonlinear attitude error components. Simulation results and analyses show that a UKF works well to handle large initial attitude errors of SDINS and the alignment error attitude estimation performance are quite improved.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.18-22
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• 2012

This paper suggests an altitude determination algorithm using GPS and barometric altitude sensors and evaluates the algorithm by digital map contour. A code based GPS altitude has lots of errors so that the car navigation companies can not use this data. Therefore, altitude is calculated by convergence data with GPS and barometric altitude variance in this paper. The modified altitudes are compared with the digital map contour and then this algorithm's effect is evaluated for the car navigation systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.528-533
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• 2005

The purpose of the study is to develop ship's bearing sensor using GPS receiver which can play a role as a ship's secondary compass. In this research, two GPS receivers are used to determine the bearing in real time. Then we investigated the bearing accuracy associated with the error pattern of two GPS receivers. Especially, the results are as follows the investigation on the system design of GPS-Compass, the modeling to compute heading of sailing, the analysis on bearing accuracy with the error pattern, the defining possibility to play a role as a ship's secondary compass.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.1
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• pp.39-45
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• 2001

The design of highway in the future should be convenient using of a high-technology information, and it needs the design of alignment that is able to find the maximum vehicles inducement function fitting into Car Navigation System. Well then, the alignment of the existent highway needs to be analyzed with accuracy for improving design of existent highway, and it needs the design drawing of existent highway, and coordinates of the main point. This study gets data of the alignment of highway economically by Kinematic GPS surveying to analyze the alignment of existent highway, and horizontal alignment of highway is analyzed by this data. The result of study is included within range practical error, and alignment analysis can be known that there is practical.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.6
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• pp.595-602
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• 2008

In this paper, performance of the initial alignment for INS whose attitude is not leveled is investigated. Observability of the initial alignment filter is analyzed and estimation errors of the estimated state variables are derived. First, the observability is analyzed using the rank test of observability matrix and the normalized error covariance of the Kalman filter based on the 10-state model. In result, it can be seen that the accelerometer biases on horizontal axes are unobservable. Second, the steady-state estimation errors of the state variables are derived using the observability equation. It is verified that the estimates of the state variables have errors due to the unobservable state variables and the non-leveling tilt angles of a vehicle containing the INS. Especially, this paper shows that the larger the tilt angles of the vehicle are, the larger the estimation errors corresponding to the sensor biases are. Finally, it is shown that the performance of the 8-state model excepting the accelerometer biases on horizontal axes is better than that of the 10-state model in the initial alignment by simulation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.31-44
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• 2004

The study of Estimation model for the short-term travel time prediction. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Before providing a dynamic shortest path finding, the prediction model should be verified. To verify the prediction model, three models such as Kalman filtering, Stochastic Process, ARIMA. The ARIMA model should adjust optimal parameters according to the traffic conditions. It requires a frequent adjustment process of finding optimal parameters. As a result of these characteristics, It is difficult to use the ARIMA model as a prediction. Kalman Filtering model has a distinguished prediction capability. It is due to the modification of travel time predictive errors in the gaining matrix. As a result of these characteristics, the Kalman Filtering model is likely to have a non-accumulative errors in prediction. Stochastic Process model uses the historical patterns of travel time conditions on links. It if favorably comparable with the other models in the sense of the recurrent travel time condition prediction. As a result, for the travel time estimation, Kalman filtering model is the better estimation model for the short-term estimation, stochastic process is the better for the long-term estimation.

• Journal of Advanced Navigation Technology
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• v.21 no.1
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• pp.43-49
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• 2017

Generally, loop filter based scalar tracking loops (LF-STL) have been used for global positioning system (GPS) signal tracking algorithm. This paper introduces the accuracy and robustness of linear-quadratic-Gaussian based vector tracking loop (LQG-VTL) algorithm instead of LF-STL. To verify the accuracy of LQG-VTL, we confirm that the measurements estimation errors of the LQG based scalar tracking loops (LQG-STL) are improved by more than 60 % compared to LF-STL. Also, when LQG-VTL is used, measurements estimation errors decrease compared to LQG-STL, and position/velocity estimation errors also decrease as the number of satellites increases. To verify the robustness of LQG-VTL, we confirm that LQG-VTL can estimate position/velocity and measurements successively compared to LF-STL in temporal signal attenuation of 30 dB-Hz during 4 seconds.