• Journal of Korean Society for Geospatial Information Science
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• v.25 no.2
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• pp.21-29
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• 2017

For the efficient and stable operation of autonomous vehicles or advanced driver assistance systems being actively studied nowadays, it is important to determine the positions of the vehicle accurately and economically. A satellite based navigation system is mainly used for positioning, but it has a limitation in signal blockage areas. To overcome this limitation, sensor fusion methods including additional sensors such as an inertial navigation system have been mainly proposed but the high sensor cost has been a problem. In this work, we develop a vehicle position estimation algorithm using in-vehicle sensors and a low-cost imaging sensor without any expensive additional sensor. We determine the vehicle positions using the velocity and yaw-rate of a car from the in-vehicle sensors and the position and attitude of the camera based on the single photo resection process. For the evaluation, we built a prototype system, acquired test data using the system, and estimated the trajectory. The proposed algorithm shows the accuracy of about 40% higher than an in-vehicle sensor only method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.4
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• pp.47-52
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• 2006

Using the equivalent ARMA model representation of the mixed random errors, we propose Klaman filter design methods for aided INS(Inertial Navigation System) which contains the gyroscope mixed random errors. At first step, considering the characteristic of indirect feedback Kalman filter used in the aided INS, we perform the time difference of equivalent ARMA model. Next, according to the order of the time differenced ARMA model, we achieve the state space conversion of that by two methods. If the order of AR part is greater than MA part, we use controllable or observable canonical form. Otherwise, we establish the state apace equation via the method that several step ahead predicts are included in the state variable, where we can derive high and low order models depending on the variable which is compensated from gyroscope output. At final step, we include the state space equation of gyroscope mixed random errors into aided INS Kalman filter model. Through the simulation, we show that both the high and low order filter models proposed give less navigation errors compared to the conventional filter which assume the mixed random errors as white noise.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.534-543
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• 2008

To use the Global Positioning System (GPS) carrier phase measurement for precise positioning, the integer ambiguities at the early stage of most algorithms must be determined. Furthermore, if a precise positioning is to be applied to real time navigation, fast determination and validation methods for integer ambiguity are essential. In this paper, the Wald test that simultaneously determines and validates integer ambiguities is used with assistance of the Inertial Navigation System (INS) to improve its performance. As the Wald test proceeds, it assigns a higher probability to the candidate that is considered to be true at each time step. The INS information is added during the Wald test process. Large performance improvements were achieved in convergence time as well as in requiring fewer observable GPS satellites. To test the performance improvement of the Wald test with the INS information, experimental tests were conducted using a ground vehicle. The vehicle moved in a prescribed trajectory and observed seven GPS satellites. To verify the effect of the INS information on the Wald test, the convergence times were compared with cases that considered the INS information and cases that did not consider the INS information. The results show that the benefits of using the INS were emphasized as fewer GPS satellites were observable. The performance improvement obtained by the proposed algorithm was shown through the fast convergence to the true hypothesis when using the INS measurements.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.11a
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• pp.328-330
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• 2018

A floating body with a device that converts solar energy into electrical energy is moved by waves. To evaluate the safety of a floating body, measurement and interpretation of the float motion is required, which is generally based on 6 degrees of freedom motion. The 6 degree of freedom motion can be measured using MEMS (Micro-Electro Mechanical System), which features low power, small size and low cost. The key issue is, meanwhile, the low precision of the MEMS. In this study, the safety evaluation technique by analyzing the behavior of floating body using MEMS was examined. As a result of the study, it was found that the marine floating body can be modeled through the inertial measurement platform using the 3-axis accelerometer and the 3-axis gyroscope, and the safety of the float can be evaluated through this model.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1349-1359
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• 2021

Acquisition of precise position and velocity information of GNSS-INS (Global Navigation Satellite System; Inertial Navigation System) sensors in obtaining SAR SLC (Single Look Complex) images from raw data using BPA (Backprojection Algorithm) was regarded decisive. Several studies on BPA were accompanied by Kalman Filter for sensor noise oppression, but often implemented once where insufficient information was given to determine whether the filtering was effectively applied. Multiple operation of Kalman Filter on GNSS-INS sensor was presented in order to assess the effective order of sensor noise calibration. FMCW (Frequency Modulated Continuous Wave)-SAR raw data was collected from twice airborne experiments whose GNSS-INS information was practically and repeatedly filtered via Kalman Filter. It was driven that the FMCW-SAR raw data with diverse path information could derive different order of Kalman Filter with optimum operation of BPA image restoration.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.23-28
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• 2019

A track type underwater construction robot(URI-R) which can trench on seabed is being developed by Korea Institute of Ocean Science & Technology. During the underwater trenching work, the robot is exposed high intensive noise and vibration so the underwater localization signal may not be obtained properly by the acoustic tracking system. Therefore it is necessary to research about continuous localization even though the measured position signal comes in intermittently. In this paper, the experiments were carried out on land to simulated the underwater operating environment characteristics. To estimate its position, inertial navigation system and global navigation satellite system are used. The effects of the period variation while localizing is investigated by the experiments, and the application for URI-R is proposed.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.441-449
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• 2021

With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.1
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• pp.1-8
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• 2004

The INS(Inertial Navigation System) provides high rate position, velocity and attitude data with good short-term stability while the GPS(Global Position System) provides position and velocity data with long-term stability. By integrating the INS with GPS, a navigation system can be achieved to Provide highly accurate navigation Performance. For the best performance, time synchronization of GPS and INS data is very important in GPS/INS integrated system But, it is impossible to synchronize them exactly due to the communication and computation time-delay. In this paper, to reduce the error caused by the measurement time-delay in GPS/INS integrated systems, error compensation methods using separate bias Kalman filter are suggested for both the loosely-coupled and the tightly-coupled GPS/INS integration systems. Linearized error models for the position and velocity matching GPS/INS integrated systems are Int derived by linearizing with respect to its time-delay and augmenting the delay-state into the conventional state equations for each case. And then separate bias Kalman Inter is introduced to estimate the time-delay during only initial navigation stage. The simulation results show that the present method is effective enough resulting in considerably less position error.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.15-26
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• 2004

The calibration for systematic error in LiDAR is crucial for the accuracy of airborne laser scanning. The main error is the misalignment of platforms between INS(Inertial Navigation System) and Laser scanner For planimetrical calibration of LiDAR, the building is good feature which has great changes in height and continuous flat area in the top. The planimetry error(pitch, roll) is corrected by adjustment of height which is calculated from comparing ground control points(GCP) of building to laser scanning data. We can know scale correction of laser range by the comparison of LiDAR data and GCP is arranged at the end of scan angle where maximize the height error. The area for scale calibration have to be large flat and have almost same elevation. At 1000m for average flying height, The Accuracy of laser scanning data using LiDAR is within 110cm in height and ${\pm}$50cm in planmetry so we can use laser scanning data for generating 3D terrain surface, expecically digital surface model(DSM) which is difficult to measure by aerial photogrammetry in forest, coast, urban area of high buildings

• Structural Engineering and Mechanics
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• v.30 no.6
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• pp.679-698
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• 2008

On marine vessels, delicate instruments such as navigation radars are normally mounted on ship masts. However the vibrations at the top of mast where the radar is mounted often cause serious deterioration in radar-tracking resolution. The most serious problem is caused by the rotational vibrations at the top of mast that may be due to wind loading, inertial loading from ship rolling and base excitations induced by the running propeller. This paper presents a method of semi-active vibration control using magneto-rheological (MR) dampers to reduce the rotational vibration of the mast. In the study, the classical optimal control algorithm, the independent modal space control algorithm and the double input - single output fuzzy control algorithm are employed for the vibration control. As the phenomenological model of an MR damper is highly nonlinear, which is difficult to analyse, a back- propagation neural network is trained to emulate the inverse dynamic characteristics of the MR damper in the analysis. The trained neural network gives the required voltage for each MR damper based on the displacement, velocity and control force of the MR damper quickly. Numerical simulations show that the proposed control methods can effectively suppress the rotational vibrations at the top of mast.