• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1332-1333
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• 2003

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.175-175
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• 2000

In this Paper we propose a navigation algorithm which can be used to estimate state vectors such as position and velocity for its motion control using multi-sensor output measurements. The output measurement we will use in estimating the state is a series of known multi-sensor asynchronous outputs with measurement noise. This paper investigates the Extended Kalman Filtering method to merge asynchronous heading, heading rate, velocity of DVL, and SSBL information to produce a single state vector. Different complexity of Kalman Filter, with biases and measurement noise, are investigated with theoretically data from KRISO's AUV. All levels of complexity of the Kalman Filters are shown to be much more close and smooth to real trajectories then the basic underwater acoustic navigation system comment)'used aboard underwater vehicle.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.4
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• pp.459-465
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• 2019

There is an increasing interest in health and research on methods for measuring human body information. The importance of continuously observing information such as the step change and the walking speed is increasing. At a person's gait, information about the disease and the currently weakened area can be known. In this paper, gait is measured using wearable walking module built in shoes. We want to make continuous measurement possible by simplifying gait measurement method. This module is designed to receive information of gyro sensor and acceleration sensor. The designed module is capable of WiFi communication and the collected walking information is stored in the server. The information stored in the server is corrected by integrating the acceleration sensor and the gyro sensor value. A band-pass filter was used to reduce the error. This data is categorized by the Gait Finder into walking and waiting states. When walking, each step is divided and stored separately for analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.2
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• pp.177-187
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• 2010

A MTG (Multimode Tracking and Guidance) system is employed to compensate for the limitations of individual seekers such as RF (Radio frequency) or IIR (Imaging Infra-red) and to improve the overall tracking and guidance performance in jamming, clutter, and adverse weather environments. In the MTG system, tracking filter, data association, and data fusion methods are important elements to maximize the effectiveness of precision homing missile guidance. This paper proposes the formulation of a Kalman filter for the estimation of line-of-sight rate from seeker measurements in missiles guided by proportional navigation. Also, we suggest the HPDA (Highest Probability Data Association) and data fusion methods of the MTG system for target tracking in the adverse environments. Mont-Carlo simulation is employed to evaluate the overall tracking performance and guidance accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.78-80
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• 2011

In the area of data fusion, dealing with heterogeneous data sources, numerous models have been proposed in last three decades to facilitate different application domains i.e. Department of Defense (DoD), monitoring of complex machinery, medical diagnosis and smart buildings. All of these models shared the theme of multiple levels processing to get more reliable and accurate information. In this paper, we consider five most widely acceptable fusion models (Intelligence Cycle, Joint Directors of Laboratories, Boyd control, Waterfall, Omnibus) applied to different areas for data fusion. When they are exposed to a real scenario, where large dataset from heterogeneous sources is utilize for object monitoring, then it may leads us to non-efficient and unreliable information for decision making. The proposed variation works better in terms of time and accuracy due to prior data diminution.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.1-11
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• 2012

Data association plays an important role in intelligent systems. This paper presents the Bayesian formulation of data association and its applications to intelligent systems. We first describe the Bayesian formulation of data association developed for solving multi-target tracking problems in a cluttered environment. Then we review applications of data association in intelligent systems, including surveillance using wireless sensor networks, identity management for air traffic control, camera network localization, and multi-sensor fusion.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.195-203
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• 2012

This paper proposes ground target classification algorithm based on decision fusion and feature extraction method using multi-sensor images. The decisions obtained from the individual classifiers are fused by applying a weighted voting method to improve target recognition rate. For classifying the targets belong to the individual sensors images, features robust to scale and rotation are extracted using the difference of brightness of CM images obtained from CCD image and the boundary similarity and the width ratio between the vehicle body and turret of target in FLIR image. Finally, we verity the performance of proposed ground target classification algorithm and feature extraction method by the experimentation.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.5
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• pp.557-564
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• 2012

This paper presents UGV localization using multi-sensor fusion based on federated filter in outdoor environments. The conventional GPS/INS integrated system does not guarantee the robustness of localization because GPS is vulnerable to external disturbances. In many environments, however, vision system is very efficient because there are many features compared to the open space and these features can provide much information for UGV localization. Thus, this paper uses the scene matching and pose estimation based vision navigation, magnetic compass and odometer to cope with the GPS-denied environments. NR-mode federated filter is used for system safety. The experiment results with a predefined path demonstrate enhancement of the robustness and accuracy of localization in outdoor environments.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.15-31
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• 2005

Nowadays, the road safety is an important research field. One of the principal research topics in this field is the vehicle localization in the road network. This article presents an approach of multi sensor fusion able to locate a vehicle with a decimeter precision. The different informations used in this method come from the following sensors: a low cost GPS, a numeric camera, an odometer and a steer angle sensor. Taking into account a complete model of errors on GPS data (bias on position and nonwhite errors) as well as the data provided by an original approach coupling a vision algorithm with a precise numerical map allow us to get this precision.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.268-273
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• 2005

We propose the specialized product-line development methodology for developing the embedded system of an MSDFS (Multi Sensor Data Fusion System : called MSDFS). The product-line methodology provides a simultaneous design between software and hardware, high level reusability. However this is insufficient in requirement analysis stage due to be focused on software architecture, detailed design and code. Thus we apply the business model based on IDEF0 technique to traditional methodology. In this paper, we describe the processes of developing Core-Asset, which are requirement analysis, feature modeling, validation. The proposed model gives the efficient result for eliciting features, and ensures the high level reusability of modules performing on embedded system.