• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.2
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• pp.97-103
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• 2005

Most car navigation systems(CNS) estimate the vehicle's location using global positioning system(GPS) or dead reckoning(DR) system. However, the estimated location has undesirable errors because of various noise sources such as unpredictable GPS noises. As a result, the measured position is not lying on the road, although the vehicle is known to be restricted on the road network. The purpose of map matching is to locate the vehicle's position on the road network where the vehicle is most likely to be positioned. In this paper, we analyze some general map matching algorithms first. Then, we propose a map matching method using compensation vectors to improve the performance of map matching. The proposed method calculates a compensation vector from the discrepancy between a measured position and an estimated position. The compensation vector and a newly measured position are to be used to determine the next estimation. To show the performance improvement of the map matching using compensation vectors, the real time map matching experiments are performed. The real road experiments demonstrate the effectiveness and applicability of the proposed map matching.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.643-644
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• 2018

Recently indoor and outdoor location tracking systems are operated in different ways. The indoor positioning method uses WiFi and BLE beacon positioning, and the outdoor positioning uses GPS and PDR. In this paper, it is a device to measure position by using it. It is used to check whether it is indoors or outdoors when measuring based on a smart phone, A automatic conversion method is needed. When using GPS in the room, it is difficult to distinguish the floor or space. We propose a method to solve this problem.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1348-1351
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• 1997

The federated Kaman filter(FKF) is being widely used in many multisensor navigatiion systems. It is know that the FKF has advantages of simplicity and fault-tolerance over other decentralized filter techniques. In this paper, optimal and suboptimal FKF configuratiions are mentioned and a covariance analysis technique for the suboptimal FKF is newly presented. The suboptimal FKF configuration, known as No-reset(NR) mode, has better fault tolerance capability than the optimal FKF coniguratioin. In the suggested technique, a suboptimal fusion process of FKF is considered a swell as suboptimal gains of local filters. An upper boun of error covariance for suboptimal FKF is derived. Also, it is mathematically shown that this bound is smaller thanexisting bound in the literatrue. A vehicle-navigaion system is designed using the FKF. In thissystem, a map constraing equation is introduced and used as a measurement equatioin of Kalman filter. Performance analysis is done by the suggested covariance analysis techniques.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.1
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• pp.33-43
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• 2018

In this paper, we describe an sound tracking mobile robot suitable for areas where GPS is not available. Sound sensors are attached to four sides of the robot in order to locate the person in a danger, and the robot is supposed to move to the yelling person. The traveling distance of the mobile robot is calculated by the encoder attached to the wheel of the mobile robot. The moving direction of the mobile robot is measured by a gyro sensor on the robot. When the person in danger pushes a button of the mobile robot, the mobile robot transmits the trajectory data to a designated server.

• Electronics and Telecommunications Trends
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• v.29 no.6
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• pp.113-125
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• 2014

최근 모바일 단말 기술의 발전과 무선망의 성능 향상에 따른 다양한 서비스가 제공되고 있는 추세이며, 위치정보인식시스템과 결합된 서비스에 많은 관심이 높아졌다. 본고에서는 GPS(Global Positioning System)의 신호가 미치지 못하는 건물의 실내환경에 적합한 경로 안내서비스 및 지하시설물 안내 등 초정밀 실내 측위 서비스를 제공하기 위한 융합 측위 방안을 제안한다. 융합 측위 방안은 실내외 연속 측위를 위해 실외에서는 GPS를 이용하고 실내환경에서는 WLAN 기반의 측위 전용 AP(Access Point)를 이용, 전파신호의 LoS(Line of Sight)를 확보하여 측위하고 전파음영지역에서는 스마트폰의 가속도, 자이로센서 등 여러 가지 관성센서를 활용하여 PDR(Pedestrian Dead Reckoning) 알고리즘 등을 적용하여 측위한다. 또한 측위 정확도 향상 및 오차를 줄이기 위한 방법으로 LSE(Least Squire Estimation) 및 EKF(Extended Kalman Filter), KNN(K-Neighbor Node)/MSSM(Maximum Signal Strength Model) Algorithm 등 다양한 융합 측위 알고리즘을 적용하여 실내환경에 적합한 최적의 초정밀 실내 측위 서비스를 제공한다.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.169-173
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• 2020

Location-based services are used as core services in various fields. In particular, in the field of public services such as emergency rescue, accurate location estimation technology is very important. Recently, the technology of tracking the location of self-isolation subjects for COVID-19 has become a major issue. Therefore, location estimation technology using personal smart devices is being studied in various ways, and the most widely used method is to use GPS. Other representative methods are using Wi-Fi, Pedestrian Dead Reckoning (PDR), Bluetooth Low Energy (BLE) beacons, and LTE signals. In this paper, we introduced a positioning technology using deep learning based on LTE Channel State Information-Reference Signal (CSI-RS) data, and confirmed the possibility through an outdoor location estimation experiment using a commercial LTE signal.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.529-536
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• 2004

Autonomous navigation of an indoor mobile robot using the global ultrasonic system is presented in this paper. Since the trajectory error of the dead-reckoning navigation grows with time and distance, the autonomous navigation of a mobile robot requires to localize the current position of the robot, so that to compensate the trajectory error. The global ultrasonic system consisting of four ultrasonic generators fixed at a priori known positions in the work space and two receivers on the mobile robot has the similar structure with the well-known satellite GPS(Global Positioning System), and it is useful for the self-localization of an indoor mobile robot. The EKF(Extended Kalman Filter) algorithm for the self-localization is proposed and the autonomous navigation based on the self-localization is verified by experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.158-163
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• 2013

The fusion of the GPS (Global Positioning System) and DR (Dead Reckoning) is widely used for position and latitude estimation of vehicles such as a mobile robot, aerial vehicle and marine vehicle. Among the many types of aerial vehicles, grater focus is given on the quad-rotor and accuracy of the position information is becoming more important. In order to exactly estimate the position information, we propose the fusion method of GPS and Gyroscope sensor using the DEKF (Dual Extended Kalman Filter). The DEKF has an advantage of simultaneously estimating state value and a parameter of dynamical system. It can also be used even if state value is not available. In order to analyze the performance of DEKF, the computer simulation for estimating the position, the velocity and the angle in a circle trajectory of quad-rotor was done. As it can be seen from the simulation results using own proposed DEKF instead of EKF on own fusion method in the navigation of a quad-rotor gave better performance values.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.481-487
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• 2013

This paper presents an FIR (Finite Impulse Response) fixed-interval smoothing filter for fast and exact estimating state variables of a discrete nonlinear system with modeling uncertainty. Conventional IIR (Infinite Impulse Response) filter and smoothing filter can estimate state variables of a system with an exact model when the system is observable. When there is an uncertainty in the system model, however, conventional IIR filter and smoothing filter may cause large errors because the filters cannot estimate the state variables corresponding to the uncertain model exactly. To solve this problem, FIR filters that have fast estimation properties and have robustness to the modeling uncertainty have been developed. However, there is time-delay estimation phenomenon in the FIR filter. The FIR smoothing filter proposed in this paper makes up for the drawbacks of the IIR filter, IIR smoothing filter, and FIR filter. Therefore, the FIR smoothing filter has good estimation performance irrespective of modeling uncertainty. The proposed FIR smoothing filter is applied to the integrated navigation system composed of a magnetic compass based DR (Dead Reckoning) and a GPS (Global Positioning System) receiver. Even when the magnetic compass error that changes largely as the surrounding magnetic field is modeled as a random constant, it is shown that the FIR smoothing filter can estimate the varying magnetic compass error fast and exactly with simulation results.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.682-687
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• 2013

Navigation is a crucial capability for all types of manned or unmanned vehicles. However, vehicle navigation in underwater environments still remains a challenging problem since GPS signals for position fixes are not available in the water. Terrain-referenced underwater navigation is an alternative navigation technique that utilizes geometric information of the subsea terrain to correct drift errors due to dead-reckoning or inertial navigation. Terrain-referenced navigation requires the description of an undulating terrain surface as a mathematical function or table, which often leads to a highly nonlinear estimation problem. Recently, PFs (Particle Filters), which do not require any restrictive assumptions about the system dynamics and uncertainty distributions, have been widely used for nonlinear filtering applications. However, PF has considerable computational requirements which used to limit its applicability to problems of relatively low state dimensions. This study proposes the use of a Rao-Blackwellized particle filter that is computationally more efficient than the standard PF for terrain-referenced underwater navigation involving a moderate number of states, and its performance is compared with that of the extended Kalman filter algorithm. The validity and feasibility of the proposed algorithm is demonstrated through numerical simulations.