• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.204-204
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• 2021

Terrestrial water storage (TWS) includes all components of water (e.g., surface water, groundwater, snow and ice) over the land. So accurately predicting and estimating TWS is important in water resource management. Although many land surface models are used to predict the TWS, model output has errors and biases in comparison to the observation data due to the model deficiencies in the model structure, atmospheric forcing datasets, and parameters. In this study, Gravity Recovery And Climate Experiment (GRACE) satelite TWS data is assimilated in the Community Land Model version 5 with a biogeochemistry module (CLM5.0-BGC) over East Asia from 2003 to 2010 by employing the Ensemble Adjustment Kalman Filter (EAKF). Results showed that TWS over East Asia continued to decrease during the study period, and the ability to simulate the surface water storage, which is the component of the CLM derived TWS, was greatly improved. We further investigated the impact of assimilated TWS on the vegetated and carbon related variables, including the leaf area index and primary products of ecosystem. We also evaluated the simulated total ecosystem carbon and calculated its correlation with TWS. This study shows that how the better simulated TWS plays a role in capturing not only water but also carbon fluxes and states.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.411-421
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• 1992

In the conventional track while scan (TWS) system, there are two major functions to be performed : detection and tracking. These two functions are normally designed and optimised independently. So TWS algorithm ignores the available decision features that can help in resolving the plot-to-track association ambiguity. Therefore conventional TWS system cna't track the targets in a densed multi-target environment. This paper presents a new TWS algorithm for multi-target track to solve the existing TWS system problem in clutter environment. The algorithm proposed in this paper is derived by modifying the part of joint probabilistic data association (JPDA) algotithm to get the one to one correspondence instead of multiple correspondence and combined with maneuvering detection logic so that it could also track the low maneuvering targets. Simulations to confirm the performance are done in crossing, parallel and maneuvering target. The proposed algorithm was successfully tracking targets above target situations.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.6
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• pp.147-155
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• 1989

In this paper, a real-time implementation of the TWS(track-while-scan) system using the high-speed DSP (digital signal processor) TMS320C25 is described. First, attempts have been made to investigate the FWL (finite word length) effect, which is caused by employing a fixed point arithmetic, of implementing the Kalman filter. The real-time TWS system consists of TWS arithmetic unit, scan converter, and system controller. In addition, the TWS system is in tegrated in the Multi-Bus. In experiment, it is observed that by employing the floating point arithmetic the computation time of 0.35sec is required for tracking 8 targets simultaneously, while 0.28sec is required for the fixed point arithmetic. Since the TWS system is designed to track up to 8 targets simultaneously, we conclude that the system is enough to process Kalman filter in a real-time.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.5
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• pp.574-580
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• 2013

Compared with the TWS(Track While Scan) tracking that uses scan-to-scan correlation at search illuminations for targets track, a phased array radar can use adaptive tracking which assigns additional track illuminations and the track formation range can be improved as a result. In this paper, an adaptive tracking, the search and track illuminations of a target are synchronized such that the extra illuminations are evenly distributed between the search illuminations, is proposed. Markov chain and track formation range for the proposed adaptive tracking are shown with them for the conventional TWS. The simulation result shows that the proposed adaptive tracking has improved track formation range by 27.6 % compared with the conventional TWS tracking under same track confirmation criterion.

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2338-2340
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• 2004

탐지레이다 또는 TWS 레이다의 측정 정밀도는 추적 필터 설계에서 quantization 문제로써 고려해야 한다. 본 논문에서는 측정 정밀도가 추적필터에서 quantization 문제로 변환됨을 보이고 오차면적과 추정성능을 비교한다. 또한 오차면적을 줄이는 방안과 quantization이 존재하면 측정잡음과 관계한 공정잡음의 power spectral density를 선정함을 보인다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.467-467
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• 2015

Sustainable water resource management requires the assessment of hydrological variability in response to climate fluctuations and anthropogenic activities. Determining quantitative estimates of water balance and total basin discharge are of utmost importance to understand the variations within a basin. Hard-to-reach areas with few infrastructures, coupled with lengthy administrative procedures makes in-situ data collection and water management processes very difficult and unreliable. In this study, the hydrological behavior of Lake Chad whose extent, extreme climatic and environmental conditions make it difficult to collect field observations was examined. During a 10 year period [January 2003 to December 2013], dataset from space-borne and global hydrological models observations were analyzed. Terrestial water storage (TWS) data retrieved from Gravity Recovery and Climate Experiment (GRACE), lake level variations from Satellite altimetry, water fluxes and soil moisture from Global Land Data Assimilation System (GLDAS) were used for this study. Furthermore, we combined altimetry lake volume with TWS over the lake drainage basin to estimate groundwater and soil moisture variations. This will be validated with groundwater estimates from WaterGAP Global Hydrology Model (WGHM) outputs. TWS showed similar variation patterns Lake water level as expected. The TWS in the basin area is governed by the lake's surface water. As expected, rainfall from GLDAS precedes GRACE TWS with a phase lag of about 1 month. Estimates of groundwater and soil moisture content volume changes derived by combining altimetric Lake Volume with TWS over the drainage basin are ongoing. Results obtained shall be compared with WaterGap Hydrology Model (WGHM) groundwater estimate outputs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.541-546
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• 2020

The surveillance radar for naval ships is an essential equipment of a battle system that executes the detection and tracking of targets, and the shooting support function; it calculates the three-dimensional track of the target range, azimuth, and altitude to carry out its duty. The surveillance radar consists of an antenna, a transceiver, a processing unit, and an air dryer section. The radar radiates the transmission signal on the antenna section, receives the reflected signal from the target, and amplifies the signals on the transceiver section. The signal received from the antenna is used to provide the operator with target information in various ways. This study identified the display problems when the information about the target is displayed through the radar. The causes of the problems were analyzed and improved. The tracking disappearance phenomenon caused by the altered-course of the ship was improved on the TWS tracking algorithm. The validity of the improved TWS tracking algorithm was confirmed by the normal condition of the target status on the B-scope.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.1-10
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• 2019

In this paper, we propose the track initiation algorithm based on the weighted score for TWS radar tracking. This algorithm utilizes radar velocity information to calculate the probabilistic track score and applies the Non-Maximum-Suppression(NMS) to confirm the targets to track. This approach is understood as a modification of a conventional track initiation algorithm in a probabilistic manner. Also, we additionally apply the weighted Hough transform to compensate a measurement error, and it helps to improve the track detection probability. We designed the simulator in order to demonstrate the performance of the proposed track initiation algorithm. The simulation result show that the proposed algorithm, which reduces about 40 % of a false track probability, is better than the conventional algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.506-509
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• 2005

An adaptive ${\alpha}-{\beta}$ tracker is proposed for tracking maneuvering targets with a track-while-scan radar system. The tracker gain is updated on-line corresponding to the adjusted process noise variance which is obtained via time averaging of the process over a sliding window. The adjusted process noise variance is used to compute the maneuverability index for the tracker gain based on the steady-state Kalman filter equation for each epoch. It is shown via simulation that the proposed approach provides robust and accurate position estimates during the target maneuver while the performance of the conventional ${\alpha}-{\beta}$ tracker is shown much degraded.