• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1001-1004
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• 1993

The Multisensor Fusion Problem (MFP) deals with the methodologies involved in effectively combining together homogeneous or non-homegeneous information obtained from multiple redundant or disparate sensors in order to perform a task more accurately, efficiently, and reliably. The inherent uncertainties in the sensory information are represented using Fuzzy Numbers, -numbers, and the Uncertainty-Reductive Fusion Technique (URFT) is introduced to combine the multiple sensory information into one consensus -number. The MFP is formulated from the Information Theory perspective where sensors are viewed as information sources with a fixed output alphabet and systems are modeled as a network of information processing and processing and propagating channels. The performance of the URFT is compared with other fusion techniques in solving the 3-Sensor Problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.327-331
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• 2006

본 논문은 엔터테인먼트 4족 보행로봇에 관한 것으로 24개의 자유도와 비접촉온도 3축 모션, 터치, 음성인식, CCD 카메라와 IR 센서를 내포하고 있다. 4족 보행로봇을 비평탄지형에서 안정되게 보행시키기 위한 알고리즘과 원격지에서 로봇을 제어하기 위한 알고리즘을 제안한다. 제안한 보행안정화 알고리즘은 다양한 센서들을 이용하여 불확실한 환경 하에서 안정된 보행이 되도록 하였으며, PC 상의 GUI와 음성인식 시스템을 이용하여 로봇을 무선으로 원격제어하게 하였다. 개발한 4족 보행로봇의 실험을 통하여 제안한 알고리즘을 검증하였다.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.

• Proceedings of the KSRS Conference
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• v.2
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• pp.989-992
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• 2006

The new approach to the problem of oil spill detection consisting in combined use of all available quasiconcurrent satellite information (AVHRR NOAA, TOPEX/Poseidon, Jason-1, MODIS Terra/Aqua, QuikSCAT) is suggested. We present the results of the application of the proposed approach to the operational monitoring of seawater condition and pollution in the coastal zone of northeastern Black Sea conducted in 2006. This monitoring is based on daily receiving, processing and analysis of data different in nature (microwave radar images, optical and infrared data), resolution and surface coverage. These data allow us to retrieve information on seawater pollution, sea surface and air-sea boundary layer conditions, seawater temperature and suspended matter distributions, chlorophyll a concentration, mesoscale water dynamics, near-surface wind and surface wave fields. The focus is on coastal seawater circulation mechanisms and their impact on the evolution of pollutants.

• Proceedings of the KSRS Conference
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• v.2
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• pp.1027-1030
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• 2006

Development of diurnal warming in the open Okhotsk Sea during the daytime and calm conditions was studied using sea surface temperature (SST) fields retrieved from NOAA AVHRR, Terra and Aqua MODIS, Aqua AMSR-E and ADEOS-II AMSR data. Sea surface wind fields were estimated from AMSR-E/AMSR measurements as well as were obtained from QuikSCAT scatterometer. Weak winds and cloudless conditions were observed in the central area of anticyclone, which moved slowly on 28-30 June 2003 east off Sakhalin. The area where the amplitude of the diurnal SST signal ${\Delta}T$ was significant also shifted slowly and had or circular or elongated shape. The ${\Delta}T$ was estimated relative to the SST values in the areas surrounding the centre of anticyclone where wind speed W exceeded 5- 6 m/s. The diurnal variations of SST, day-night differences were computed using NOAA-12 and NOAA-16 AVHRRderived data. Analysis of simultaneous SST and W fields showed that the increase of W from 0 to 5-6 m/s causes the decrease of ${\Delta}T$ to zero. Maximum warming exceeded $8^{\circ}C$ and was observed in the centre of anticyclone where W = 0 m/s. So strong heating was likely due to the increased chlorophyll a concentration in the area under study that follows from analysis of satellite ocean colour data.

• ETRI Journal
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• v.33 no.4
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• pp.506-516
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• 2011

In this paper, we develop a registration method to eliminate the geometric inconsistency between the stereo-images and light detection and ranging (LIDAR) data obtained by an airborne multisensor system. This method consists of three steps: registration primitive extraction, correspondence establishment, and exterior orientation parameter (EOP) adjustment. As the primitives, we employ object points and linked edges from the stereo-images and planar patches and intersection edges from the LIDAR data. After extracting these primitives, we establish the correspondence between them, being classified into vertical and horizontal groups. These corresponding pairs are simultaneously incorporated as stochastic constraints into aerial triangulation based on the bundle block adjustment. Finally, the EOPs of the images are adjusted to minimize the inconsistency. The results from the application of our method to real data demonstrate that the inconsistency between both data sets is significantly reduced from the range of 0.5 m to 2 m to less than 0.05 m. Hence, the results show that the proposed method is useful for the data fusion of aerial images and LIDAR data.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.377-382
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• 2006

Precision approach and landing of aircraft in a remote landing zone autonomously present several challenges. Firstly, the exact location, orientation and elevation of the landing zone are not always known; secondly, the accuracy of the navigation solution is not always sufficient for this type of precision maneuver if there is no DGPS availability within close proximity. This paper explores an alternative approach for estimating the navigation parameters of the aircraft to the landing area using only time-differenced GPS carrier phase measurement and range measurements from a vision system. Distinct ground landmarks are marked before the landing zone. The positions of these landmarks are extracted from the vision system then the ranges relative to these locations are used as measurements for the extended Kalman filter (EKF) in addition to the precise time-differenced GPS carrier phase measurements. The performance of this navigation algorithm is demonstrated using simulation.

• Korean Journal of Remote Sensing
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• v.11 no.2
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• pp.59-73
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• 1995

The study is focused on the analysis of geomorphological environment changes of alluvial bar in lower Kum river using satellite-based multitemporal/multisensor data. Landsat datas for environment changes analysis consists of Landset MSS(2 scenes) and Landset TM(7 scenes) acquired from 1979 to 1994. This study is to develop the analysis techniques for the environment change detection of using ratio, classification, false color composite etc, of Landsat data especially useful to the geomorphological study of tidal flats and river channels. The results of this study can be summarized as follows : 1. The lower Kum River alluvial bar have had rapid geomorphological changes after the construction of the temporary dam to block the river flowing in 1983. The most alluvial bar located in the river has both bankway growth, especially the allurival bar in the Lower Kum River had grown between 1983 to 1990. 2. After construction of the estuarine barrage, no remarkable geomorphological changes have been found in Kum River area but the growth and formation of new underwater bar has continued. The enormous materials was needed for the growth and formations of new underwater barrier oslands and bar would be supplied from the sea bottom and river sediment to diminish of stream velocity after construction of the estuarine barrage.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.351-361
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• 2003

As the launches of a series of remote sensing satellites, there are various multiresolution and multi-spectral images available nowadays. This diversity in remotely sensed image data has created a need to be able to integrate data from different sources. The C-band imaging radar of ERS-2 due to its high sensitivity to coastal wetlands holds tremendous potential in mapping and monitoring coastal wetland features. This paper investigates the advantages of using ERS-2 SAR data combined with IRS-ID LISS-3 data for mapping complex coastal wetland features of Tamil Nadu, southern India. We present a methodology in this paper that highlights the mapping potential of different combinations of filtering and integration techniques. The methodology adopted here consists of three major steps as following: (i) speckle noise reduction by comparative performance of different filtering algorithms, (ii) geometric rectification and coregistration, and (iii) application of different integration techniques. The results obtained from the analysis of optical and microwave image data have proved their potential use in improving interpretability of different coastal wetland features of southern India. Based visual and statistical analyzes, this study suggests that brovey transform will perform well in terms of preserving spatial and spectral content of the original image data. It was also realized that speckle filtering is very important before fusing optical and microwave data for mapping coastal mangrove wetland ecosystem.