• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.174-177
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• 1995

Detecting a small threat object either fast moving or floating on shallow water presents a formidable challenge to shipboard sensor systems, which must determine whether or not to launch defensive weapons in a timely manner. An integrated multisensor concept is envisioned wherein the combined use of active and passive sensor is employed for the detection of short duration targets in dense ocean surface clutter to maximize detection range. The objective is to develop multisensor integration techniques that operate on detection data prior to track formation while simultaneously fusing contacts to tracks. In the system concept, detections from a low grazing angle search radar render designations to a sensor-search infrared sensor for target classification which in turn designates an active electro-optical sensor for sector search and target verification.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.679-684
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• 1993

This article presents an approach to estimate the general 3D motion of a polyhedral object using multiple, sensory data some of which may not provide sufficient information for the estimation of object motion. Motion can be estimated continuously from each sensor through the analysis of the instantaneous state of an object. We have introduced a method based on Moore-Penrose pseudo-inverse theory to estimate the instantaneous state of an object. A linear feedback estimation algorithm is discussed to estimate the object 3D motion. Then, the motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown object. The techniques of multisensor data fusion can be categorized into three methods: averaging, decision, and guiding. We present a fusion algorithm which combines averaging and decision.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.22-28
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• 1992

The need for intelligent systems that can operate in an unstructured, dynamic environment has created a growing demand for the use of multiple, distributed sensors. While most research in multisensor fusion has revolved around applications in object recognition-including military applications for automatic target recognition-developments in microsensor technology are encouraging more research in affordable, highly-redundant sensor networks. Three trends that are described at length are the increasing use of microsensors, the techniques that are used in the handling of partial or uncertain data, and the application of neural network techniques for sensor fusion.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.75-85
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• 2006

This paper has dealt with a data fusion for the problem of land-cover classification using multisensor imagery. Dempster-Shafer evidence theory has been employed to combine the information extracted from the multiple data of same site. The Dempster-Shafer's approach has two important advantages for remote sensing application: one is that it enables to consider a compound class which consists of several land-cover types and the other is that the incompleteness of each sensor data due to cloud-cover can be modeled for the fusion process. The image classification based on the Dempster-Shafer theory usually assumes that each sensor is represented by a single channel. The evidential approach to image classification, which utilizes a mass function obtained under the assumption of class-independent beta distribution, has been discussed for the multiple sets of mutichannel data acquired from different sensors. The proposed method has applied to the KOMPSAT-1 EOC panchromatic imagery and LANDSAT ETM+ data, which were acquired over Yongin/Nuengpyung area of Korean peninsula. The experiment has shown that it is greatly effective on the applications in which it is hard to find homogeneous regions represented by a single land-cover type in training process.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.91.4-91
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• 2001

In this paper, a development of the sensor fusion algorithm for a visual control of mobile robot is presented. The output data from the visual sensor include a time-lag due to the image processing computation. The sampling rate of the visual sensor is considerably low so that it should be used with other sensors to control fast motion. The main purpose of this paper is to develop a method which constitutes a sensor fusion system to give the optimal state estimates. The proposed sensor fusion system combines the visual sensor and inertial sensor using a modified Kalman filter. A kind of multi-rate Kalman filter which treats the slow sampling rate ...

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1464-1468
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• 1997

We propose a decentralized state estimation method in the multisensor state estimation problem. The proposed method bounds teh maximum energy gain from uknown external disturbances to the estimation errors in the suboptimal case. And we formulate aternative H/sip .inf./ filter gain equatiions with teh idea that the suboptimal H.$^{\infty}$ filter is the special form of Kalman filter filter whose state equations are defined in indefinite inner product spaces. Using alternative filter gain equations we design the decentralized $H^{\infty}$ state estimator which is composed of local filters and central fusion filter that are suboptimal in the $H^{\infty}$ sense. In addition, the proposed update equations between global and local data can reduce unnecessary calculation burden efficently.y.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1597-1600
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• 1991

In this paper, we describe a notion of sensor modeling method in multisensor data fusion using fuzzy set theory. Each sensor module is characterized by its fuzzy constraints to specific features of environment. These sensor fuzzy constraints can be imposed on multisensory data to verify their degree of truth and compatibility toward the final decision making. In comparison with other sensor modeling methods, such as probabilistic models or rule-based models, the proposed method is very simple and can be easily implemented in intelligent robot systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.178-185
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• 2011

In this paper, we propose the position estimation algorithm based on the multisensor fusion using equalization of state variables and feedback structure. First, the state variables measured from INS of main sensor with large error and DVL of assistance sensor with small error are measured before prediction phase. Next, the equalized state variables are entered to each filter and fused the enhanced state variables for prediction and update phases. Finally, the fused state variables are returned to the main sensor for improving the position estimation of UUV. For evaluation, we create the moving course of UUV by simulation and confirm the performance of position estimation by applying the proposed algorithm. The evaluation results show that the proposed algorithm is the best for position estimation and also possible for robust position estimation at the change period of moving courses.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.5
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• pp.676-686
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• 2012

This paper presents a sensor bias estimation method with serial fusion for asynchronous multisensory systems. Serial fusion processes the sensor measurements in a first-come-first-serve basis and it plays an essential role in asynchronous fusion in practice. The proposed algorithm generates the bias measurements using fusion estimates and sensor measurements for bias estimation, and compensates the sensor biases in fusion tracks. A simulation study indicates that the proposed algorithm has the superior performance in bias estimation and accurate tracking.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.329-339
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• 2003

In this study, an approach of image fusion in decision level has been proposed for unsupervised image classification using the images acquired from multiple sensors with different characteristics. The proposed method applies separately for each sensor the unsupervised image classification scheme based on spatial region growing segmentation, which makes use of hierarchical clustering, and computes iteratively the maximum likelihood estimates of fuzzy class vectors for the segmented regions by EM(expected maximization) algorithm. The fuzzy class vector is considered as an indicator vector whose elements represent the probabilities that the region belongs to the classes existed. Then, it combines the classification results of each sensor using the fuzzy class vectors. This approach does not require such a high precision in spatial coregistration between the images of different sensors as the image fusion scheme of pixel level does. In this study, the proposed method has been applied to multispectral SPOT and AIRSAR data observed over north-eastern area of Jeollabuk-do, and the experimental results show that it provides more correct information for the classification than the scheme using an augmented vector technique, which is the most conventional approach of image fusion in pixel level.