• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.702-708
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• 2013

Underwater TRN (Underwater Terrain Referenced Navigation) estimates an underwater vehicle state by measuring a distance between the vehicle and undersea terrain, and comparing it with the known terrain database. TRN belongs to absolute navigation methods, which are used to compensate a drift error of dead reckoning measurements such as IMU (Inertial Measurement Unit) or DVL (Doppler Velocity Log). However, underwater TRN is different to other absolute methods such as USBL (Ultra-Short Baseline) and LBL (Long Baseline), because TRN is independent of the external environment. As a magnetic-field-based navigation, TRN is a kind of geophysical navigation. This paper develops an EKF (Extended Kalman Filter) formulation for underwater TRN. A filter propagation part is composed by an inertial navigation system, and a filter update is executed with echo-sounder measurement. For large-initial-error cases, an adaptive EKF approach is also presented, to keep the filter be stable. At the end, simulation studies are given to verify the performance of the proposed TRN filter. With simplified sensor and terrain database models, the simulation results show that the underwater TRN could support conventional underwater navigation methods.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.7
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• pp.633-643
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• 2005

An unmanned aerial vehicle (UAV) is an aircraft controlled by .emote commands from ground station and/o. pre-programmed onboard autopilot system. A navigation system in the UAV provides a navigation data for a flight control computer(FCC). The FCC requires accurate and reliable position, velocity and attitude information for guidance and control. This paper proposes an ADGPS/INS integrated navigation system for a UAV. The proposed navigation system comprises an attitude determination GPS (ADGPS) receive., a navigation computer unit, and a low-cost commercial MEMS inertial measurement unit(IMU). The navigation algorithm contains a fault detection and isolation (FDI) function fur integrity. In order to evaluate the performance of the proposed navigation system, two flight tests were preformed using a small aircraft. The first flight test was carried out to confirm fundamental operation of the proposed navigation system and to check the effectiveness of the FDI algorithm. In the second flight test, the navigation performance and the benefit of the GPS attitude information were checked in a high dynamic environment. The flight test results show that the proposed ADGPS/INS integrated navigation system gives a reliable performance even when anomalous GPS data is provided and better navigation performance than a conventional GPS/INS integration unit.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1589-1597
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• 2021

Due to the low output rate and time delay of vehicle's navigation results, the electro-optical tracking system(EOTS) cannot estimate accurate target positions. If an inertial measurement unit(IMU) is additionally mounted into the EOTS and inertial navigation system(INS) is constructed, the high navigation output rate can be obtained. And the time-delay can be compensated by using the augmented Kalman filter. An accurate initial attitude is required in order to have accurate navigation outputs. In this paper, an attitude determination algorithm is proposed using the augmented Kalman filter in order to compensate measurement delay of the EOTS and have accurate initial attitude. The proposed initial attitude determination algorithm consists of an augmented Kalman filter, an INS, and an integrated Kalman filter. The augmented Kalman filter compensates the time-delay of the vehicle's navigation results and the integrated Kalman filter estimates the navigation error of the INS. In order to evaluate performance of the proposed algorithm, vehicle's navigation outputs and IMU measurements were generated using sensors' model-based measurement generator and initial attitude estimation errors of the proposed algorithm and the conventional algorithm without the augmented Kalman filter were compared for the generated measurements. The evaluation results show that the proposed algorithm has better accuracy.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.4
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• pp.181-194
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• 2017

In recent years, research has been actively conducted on the navigation in an indoor environment where Global Navigation Satellite System signals are unavailable. Among them, a study performed indoor navigation by integrating pseudolite carrier and Inertial Measurement Unit (IMU) sensor. However, in this case, there was no solution for the cycle slip occurring in the carrier. In another study, cycle slip detection and compensation were performed by integrating Global Positioning System (GPS) and IMU in an outdoor environment. However, in an indoor environment, cycle slip occurs more easily and frequently, and thus the occurrence of half cycle slip also increases. Accordingly, cycle slip detection based on 1 cycle unit has limitations. Therefore, in the present study, the aforementioned problems were resolved by performing indoor navigation through the integration of pseudolite and ultra-low-cost IMU embedded in a smartphone and by performing half cycle slip detection and compensation based on this. In addition, it was verified through the actual implementation of real-time navigation.

• Journal of Positioning, Navigation, and Timing
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• v.5 no.3
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• pp.119-129
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• 2016

This paper presents an integrated navigation system for accurate navigation solution-based safety and convenience services in the vehicular augmented reality (AR)-head up display (HUD) system. For lane-level guidance service, especially, an accurate navigation system is essential. To achieve this, an inertial navigation system (INS)/global positioning system (GPS)/vision/digital map (IGVM) integrated navigation system has been developing. In this paper, the concept of the integrated navigation system is introduced and is implemented based on a multi-model switching filter and vehicle status decided by using the GPS data and inertial measurement unit (IMU) measurements. The performance of the implemented navigation system is verified experimentally.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.884-887
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• 2023

Recently, research has been actively conducted to overcome the limitations of high-priced single sensors and reduce costs through the convergence of low-cost multi-variable sensors. This paper estimates state variables through asynchronous Kalman filters constructed using CVXPY and uses Cvxpylayers to compare and learn state variables estimated from CVXPY with true value data to estimate filter parameters of low-cost sensors fusion.

• 전자공학회논문지 IE
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• v.45 no.4
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• pp.11-18
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• 2008

This study is implementation of attitude control system(ACS - Attitude Control System). for a multi legs robot. This study designs H/W of Inertial Measurement Unit (IMU) and attitude control algorithm S/W. Compare performance with Mtx and MTx in order to verify action performance of this system after implementation, and will verify a system integrated IMU of a multi-legs robot. ACS uses Gyro and an accelerometer and an earth magnetism sensor, and it is a system controlling a roll, pitch angle attitude of an object. Generally, low price MEMS is difficult to calculate a correct situation of an object as an error occurs severely the Inertial sensor. This study implements IMU in order to develop ACS as use MEMS, accelerometer, Gyro sensor and earth magnetism sensor. Design algorithm each a roll, pitch, yaw attitude guaranteeing regular performance, and do poling in a system as include an attitude calculation program in an IMU system implemented. Mixed output of Gyro and an accelerometer, and recompensed a roll, pitch angle, and loaded in this study on a target platform in order to implement the ACS which guaranteed performance more than a continuously regular level, and operated by real time, and did porting, and verified.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.768-773
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• 2020

In large-scale land development for the rational use and management of national land resources, the use of geospatial information is essential for the efficient management of projects. Recently, drone LiDAR (Light Detection And Ranging) has attracted attention as an effective geospatial information construction technique for large-scale development areas, such as housing site construction and open-pit mines. Drone LiDAR can be classified into a method using SLAM (Simultaneous Localization And Mapping) technology and a GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit) method. On the other hand, there is a lack of analytical research on the application of drone LiDAR or the characteristics of each method. Therefore, in this study, data acquisition, processing, and analysis using SLAM and GNSS/IMU type drone LiDAR were performed, and the characteristics and utilization of each were evaluated. As a result, the height direction accuracy of drone LiDAR was -0.052~0.044m, which satisfies the allowable accuracy of geospatial information for mapping. In addition, the characteristics of each method were presented through a comparison of data acquisition and processing. Geospatial information constructed through drone LiDAR can be used in several ways, such as measuring the distance, area, and inclination. Based on such information, it is possible to evaluate the safety of large-scale development areas, and this method is expected to be utilized in the future.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1015-1023
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• 2022

The Entry-Descent-Landing process of a lander involves many environmental and technical challenges. To solve these problems, recently, terrestrial relative navigation (TRN) technology has been essential for landers. TRN is a technology for estimating the position and attitude of a lander by comparing Inertial Measurement Unit (IMU) data and image data collected from a descending lander with pre-built reference data. In this paper, we present a method for generating descent dataset and extracting landmarks, which are key elements for developing TRN technologies to be used on Mars. The proposed method generates IMU data of a descending lander using a simulated Mars landing trajectory and generates descent images from high-resolution ortho-map and digital elevation map through a ray tracing technique. Landmark extraction is performed by an area-based extraction method due to the low-textured surfaces on Mars. In addition, search area reduction is carried out to improve matching accuracy and speed. The performance evaluation result for the descent dataset generation method showed that the proposed method can generate images that satisfy the imaging geometry. The performance evaluation result for the landmark extraction method showed that the proposed method ensures several meters of positioning accuracy while ensuring processing speed as fast as the feature-based methods.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.562-569
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• 2016

This paper proposes a navigation system with a robust localization method for an underwater unmanned vehicle. For robust localization with IMU (Inertial Measurement Unit), a DVL (Doppler Velocity Log), and depth sensors, the EKF (Extended Kalman Filter) has been utilized to fuse multiple nonlinear data. Note that the GPS (Global Positioning System), which can obtain the absolute coordinates of the vehicle, cannot be used in the water. Additionally, the DVL has been used for measuring the relative velocity of the underwater vehicle. The DVL sensor measures the velocity of an object by using Doppler effects, which cause sound frequency changes from the relative velocity between a sound source and an observer. When the vehicle is moving, the motion trajectory to a target position can be recorded by the sensors attached to the vehicle. The performance of the proposed navigation system has been verified through real experiments in which an underwater unmanned vehicle reached a target position by using an IMU as a primary sensor and a DVL as the secondary sensor.