• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.325-330
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• 2022

The sensors of aircraft are necessity for mission performance and fusion process of data from them is applied for increase of mission efficiency and decrease of aircraft pilot workload. Data fusion is applied and developed to provide pilot a series of more processed data format about a specific target from sensors in aircraft. Military aircraft currently in operation are linked with a tactical data link such as Link-16 to display improved tactical situation to pilots to increase mission efficiency. By fusing the sensor data with improved accuracy obtained as the sensors' performance mounted on the aircraft become higher and the tactical situation information received through the tactical data link, it provides the pilot with a highly reliable tactical situation and mission environment, and expects efficient mission performance and high survivability. In this paper, a fusion architecture to produce fused data with realtime information from the sensors and data through a tactical data link is shown.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.283-285
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• 2005

To account for the importance of variable in multi-sensor data fusion, random forests are applied to supervised land-cover classification. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. Its distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Supervised classification with a multi-sensor remote sensing data set including optical and polarimetric SAR data was carried out to illustrate the applicability of random forests. From the experimental result, the random forests approach could extract important variables or bands for land-cover discrimination and showed good performance, as compared with other non-parametric data fusion algorithms.

• The Journal of Korea Robotics Society
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• v.2 no.3
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• pp.255-261
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• 2007

We propose a optimal fusion method for localization of multiple robots utilizing correlation between GPS on each robot in common workspace. Each mobile robot in group collects position data from each odometer and GPS receiver and shares the position data with other robots. Then each robot utilizes position data of other robot for obtaining more precise estimation of own position. Because GPS data errors in common workspace have a close correlation, they contribute to improve localization accuracy of all robots in group. In this paper, we simulate proposed optimal fusion method of odometer and GPS through virtual robots and position data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1042-1044
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• 2003

A methodology of the integrating the similar color circle search of the spectral data and segmentation of the height data is developed. The method is then applied to study areas, and the results by IKONOS, LIDAR and data fusion are verified with the ground truth, and examined in terms of the accuracy. Results show that with the data fusion the accuracy are improved by about 15% in most of the study areas. The methodology for the detection of individual tree stands by data fusion is explored, and the utility of combinatorial use of the spectral and the height information is demonstrated.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.91.3-91
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• 2001

A sensor fusion scheme for mobile robot environment recognition that incorporates range data and contour data is proposed. Ultrasonic sensor provides coarse spatial description but guarantees open space with no obstacle within sonic cone with relatively high belief. Laser structured light system provides detailed contour description of environment but prone to light noise and is easily affected by surface reflectivity. Overall fusion process is composed of two stages: Noise elimination and belief updates. Dempster Shafer´s evidential reasoning is applied at each stage. Open space estimation from sonar range measurements brings elimination of noisy lines from laser sensor. Comparing actual sonar data to the simulated sonar data enables ...

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.551-553
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• 1999

This paper explores the utility of a new classifier fusion approach to discrimination. Multiple classifier fusion, a popular approach in the field of pattern recognition, uses estimates of each individual classifier's local accuracy on training data sets. In this paper we investigate the effectiveness of fusion methods compared to individual algorithms, including the artificial neural network and k-nearest neighbor techniques. Moreover, we propose an efficient meta-classifier architecture based on an approximation of the posterior Bayes probabilities for learning the oracle.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.87-100
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• 2004

When spatial data which a user requests are stored various data servers in web mapping environment, it is necessary to access one more map servers to gain data and then integrate them. This paper designs and implements a fusion map server for web mapping. The proposed fusion map server has three characteristics. First, it has customizing metadata which reflect various user's requests. Second, it provides fusion services which integrate spatial and non-spatial data. Last, it uses the dynamic liking method to support the fusion services efficiently. We design and implement a publisher client, a map server and a fusion map server for proposed map server.

• Journal of Astronomy and Space Sciences
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• v.20 no.4
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• pp.359-364
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• 2003

In this paper, we present multi-sensor data fusion for telematics application. Successful telematics can be realized through the integration of navigation and spatial information. The well-determined acquisition of vehicle's position plays a vital role in application service. The development of GPS is used to provide the navigation data, but the performance is limited in areas where poor satellite visibility environment exists. Hence, multi-sensor fusion including IMU (Inertial Measurement Unit), GPS(Global Positioning System), and DMI (Distance Measurement Indicator) is required to provide the vehicle's position to service provider and driver behind the wheel. The multi-sensor fusion is implemented via algorithm based on Kalman filtering technique. Navigation accuracy can be enhanced using this filtering approach. For the verification of fusion approach, land vehicle test was performed and the results were discussed. Results showed that the horizontal position errors were suppressed around 1 meter level accuracy under simulated non-GPS availability environment. Under normal GPS environment, the horizontal position errors were under 40㎝ in curve trajectory and 27㎝ in linear trajectory, which are definitely depending on vehicular dynamics.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.487-494
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• 2011

Data Fusion algorithm is essential in Target Detection using radar, and it has more reliability. In this paper, Multi Radar Fusion algorithm using IMM(Interacting Multiple Model) filter is suggested. This well-known IMM filter has better performance than Kalman filter has. In this simulation, Distributed Data Fusion process was applied, and three sub-filters and one main filter were employed. In addition, this simulation was evaluated by virtual radar data which include constant velocity, constant accelerate, turn rate. The result of an evaluation shows better performance in the maneuvering section of aircraft.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.