• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.298-304
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• 2009

This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (${\Phi}$, ${\Delta}$) and a camera calibration matrix (K). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.9
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• pp.414-419
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• 2006

This paper presents a design of a multi-sensor data fusion filter for a Flight Test System. The multi-sensor data consist of positional information of the target from radars and a telemetry system. The data fusion filter has a structure of a federated Kalman filter and is based on the Singer dynamic target model. It consists of dedicated local filter for each sensor, generally operating in parallel, plus a master fusion filter. A fault detection and correction algorithms are included in the local filter for treating bad measurements and sensor faults. The data fusion is carried out in the fusion filter by using maximum likelihood estimation algorithm. The performance of the designed fusion filter is verified by using both simulation data and real data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.1
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• pp.5-24
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• 2010

Data fusion is an attractive technology because it allows various trade-offs related to performance metrics, e.g., energy, latency, accuracy, fault-tolerance and security in wireless sensor networks (WSNs). Under a complicated environment, each sensor node must be equipped with more than one type of sensor module to monitor multi-targets, so that the complexity for the fusion process is increased due to the existence of various physical attributes. In this paper, we first investigate the process and performance of multi-attribute fusion in data gathering of WSNs, and then propose a self-adaptive threshold method to balance the different change rates of each attributive data. Furthermore, we present a method to measure the energy-conservation efficiency of multi-attribute fusion. Based on our proposed methods, we design a novel energy equilibrium routing method for WSNs, viz., multi-attribute fusion tree (MAFT). Simulation results demonstrate that MAFT achieves very good performance in terms of the network lifetime.

• Journal of National Security and Military Science
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• s.1
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• pp.191-229
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• 2003

Multi-sensor data fusion techniques combine evidences from multiple sensors in order to get more accurate and efficient meaningful information through several process levels that may not be possible from a single sensor alone. One of the most important parts in the data fusion system is the identification fusion, and it can be categorized into physical models, parametric classification and cognitive-based models, and parametric classification technique is usually used in multi-sensor data fusion system by its characteristic. In this paper, we propose a novel heuristic identification fusion method in which we adopt desirable properties from not only parametric classification technique but also cognitive-based models in order to meet the realtime processing requirements.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1551-1558
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• 2014

We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor's predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.4
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• pp.581-587
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• 2011

A wireless sensor network needs to consist of multi-sensors in order to infer a high level of information on circumstances. Data fusion, in turn, is required to utilize the data collected from multi-sensors for the inference of information on circumstances. The current paper, based on Dempster-Shafter's evidence theory, proposes data fusion in a wireless sensor network with different weights assigned to different sensors. The frequency of events per sensor is the crucial element in calculating different weights of the data of circumstances that each sensor collects. Data fusion utilizing these different weights turns out to show remarkable difference in reliability, which makes it much easier to infer information on circumstances.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.783-789
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• 2011

Target tracking performance improvement using multi-sensor data fusion is a challenging work. However, biases in the measurements should be removed before various data fusion techniques are applied. In this paper, a bias removing algorithm using measurement data from multi-radar tracking systems is proposed and evaluated by computer simulation. To predict bias estimation performance in various geometric relations between the radar systems and target, a system observability index is proposed and tested via computer simulation results. It is also studied that target tracking which utilizes multi-sensor data fusion with bias-removed measurements results in better performance.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.999-1011
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• 2021

In spatio-temporal fusion aiming at predicting images with both high spatial and temporal resolutionsfrom multi-sensor images, the radiometric inconsistency between input multi-sensor images may affect prediction performance. This study investigates the effect of radiometric correction, which compensate different spectral responses of multi-sensor satellite images, on the spatio-temporal fusion results. The effect of relative radiometric correction of input images was quantitatively analyzed through the case studies using Sentinel-2, PlanetScope, and RapidEye images obtained from two croplands. Prediction performance was improved when radiometrically corrected multi-sensor images were used asinput. In particular, the improvement in prediction performance wassubstantial when the correlation between input images was relatively low. Prediction performance could be improved by transforming multi-sensor images with different spectral responses into images with similar spectral responses and high correlation. These results indicate that radiometric correction is required to improve prediction performance in spatio-temporal fusion of multi-sensor satellite images with low correlation.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.397-402
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• 2013

Time variation is the essential component of the context awareness. It is a beneficial way not only including time lapse but also clustering time interval for the context inference using the information from sensor mote. In this study, we proposed a novel way of clustering based multi-sensor data fusion for the context inference. In the time interval, we fused the sensed signal of each time slot, and fused again with the results of th first fusion. We could reach the enhanced context inference with assessing the segmented signal according to the time interval at the Dempster-Shafer evidence theory based multi-sensor data fusion.