• Journal of Advanced Navigation Technology
• /
• v.26 no.6
• /
• pp.487-495
• /
• 2022

This paper describes the design and development of a high-power transmit receive module(TRM) for mounting on X-band synthetic aperture radar(SAR) of the NEXTSat-2. The TRM generates a high-power pulse signal with a bandwidth of 100 MHz in the target frequency range of X-band and amplifies a low-noise on the received signal. Tx. path of the TRM has output signal level of more than 200 watts (53.01 dB), pulse droop of 0.35 dB, signal strength change of 0.04 dB during transmission signal output, and phase change of 1.7 ˚. Rx. path has noise figure of 3.99 dB and gain of 37.38 ~ 37.46 dB. It was confirmed the TRM satisfies all requirements. The TRM mounted on the NEXTSat-2 flight model(FM) which will be launched using the KSLV-II (Nuri).

• Journal of Aerospace System Engineering
• /
• v.18 no.4
• /
• pp.96-101
• /
• 2024

The Synthetic Aperture Radar (SAR) is equipped with a Transmitter/Receiver (TR) module, which serves as the signal transmission and reception unit for acquiring image data. The TR module generates significant heat during signal generation and amplification, potentially degrading performance or causing mission failure. Furthermore, launch and operational environments may result in structural damage to the components. Thus, assessing the thermal and structural safety of the TR module through thermal and vibration tests is essential to guarantee its safety. Safety assessments can be verified through environmental tests prescribed in MIL-STD-883. This paper explores the thermal and structural safety characteristics of the TR module by simulating test environments using finite element analysis prior to conducting environmental tests.

• Journal of Korean Port Research
• /
• v.14 no.3
• /
• pp.353-360
• /
• 2000

In this study a drift prediction model based on fluid dynamics theory is introduced. The essential effects of environmental loads and target characteristics are taken into account from a fluid dynamics point of view. The governing equations of motion are derived from Netwon's law of dynamics. In the mathematical formulation only three degrees of freedom(surge, sway, yaw) of the drifting object are assumed and the environmental loads considered are the forces and moments by wind and current. A computer algorithm for this model is implemented to obtain the numerical result in the time domain. The preliminary tests for model verification are conducted and the results are compared with the field experiment data as well as leeway formula suggested from the field test data.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2000.11a
• /
• pp.63-71
• /
• 2000

In this study a drift prediction model based on fluid dynamics theory is introduced. The essential effects of environmental loads and target characteristics are taken into account from a fluid dynamics point of view. The governing equations of motion are derived from Newton's law of dynamics. In the mathematical formulation only three degrees of freedom(surge, sway, yaw) of the drifting object are assumed and the environmental loads considered are the forces and moments by wind and current. A computer algorithm for this model is implemented to obtain the numerical result in the time domain. The preliminary tests for model verification are conducted and the results are compared with the field experiment data as well as leeway formula suggested from the field test data.

• Korean Journal of Remote Sensing
• /
• v.19 no.5
• /
• pp.363-370
• /
• 2003

This paper describes a rectification procedure that relies on a polynomial model derived from the imaging geometry without loss of accuracy. By using polynomial model, one can effectively eliminate the iterative process to find an image pixel corresponding to each output grid point. With the imaging geometry and ephemeris data, a geo-location polynomial can be constructed from grid points that are produced by solving three equations simultaneously. And, in order to correct the local distortions induced by the geometry and terrain height, a distortion model has been incorporated in the procedure, which is a function of incidence angle and height at each pixel position. With this function, it is straightforward to calculate the pixel displacement due to distortions and then pixels are assigned to the output grid by re-sampling the displaced pixels. Most of the necessary information for the construction of polynomial model is available in the leader file and some can be derived from others. For validation, sample images of ERS-l PRI and Radarsat-l SGF have been processed by the proposed method and evaluated against ground truth acquired from 1:25,000 topography maps.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.6
• /
• pp.1-13
• /
• 2021

In this study, a time series analysis technique was applied to calibrate and predict traffic data for various purposes, such as planning, design, maintenance, and research. Existing algorithms have limitations in application to data such as traffic data because they show strong periodicity and seasonality or irregular data. To overcome and supplement these limitations, we applied the SARIMA model, an analytical technique that combines the autocorrelation model, the Seasonal Auto Regressive(SAR), and the seasonal Moving Average(SMA). According to the analysis, traffic volume prediction using the SARIMA(4,1,3)(4,0,3) 12 model, which is the optimal parameter combination, showed excellent performance of 85% on average. In addition to traffic data, this study is considered to be of great value in that it can contribute significantly to traffic correction and forecast improvement in the event of missing traffic data, and is also applicable to a variety of time series data recently collected.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1589-1605
• /
• 2022

Phase unwrapping is an essential procedure for interferometric synthetic aperture radar techniques. Accordingly, a lot of phase unwrapping methods have been developed. Deep-learning-based unwrapping methods have recently been proposed. In this paper, we reviewed state-of-the-art deep-learning-based unwrapping approaches in terms of 1) the approaches to predicting unwrapped phases, 2) deep learning model structures for phase unwrapping, and 3) training data generation. The research trend of the approaches to predicting unwrapped phases was introduced by categorizing wrap count segmentation, phase jump classification, phase regression, and deep-learning-assisted method. We introduced the case studies of deep learning model structure for phase unwrapping, and model structure optimization to relate the overall phase information. In addition, we summarized the research trend of the training data generation approaches in the views of phase gradient and noise in the main. And the future direction in deep-learning-based phase unwrapping was presented. It is expected that this paper is used as guideline for exploring future direction of deep-learning-based phase unwrapping research in Korea.

• Journal of Advanced Navigation Technology
• /
• v.28 no.2
• /
• pp.193-200
• /
• 2024

This paper describes the design and development of a transmit receiver module(TRM) for mounting on X-band SAR of the NEXTSat-2. The TRM generates the chirp signal with required bandwidth through the DDS in X-band and performs frequency conversion, combination for the signal to transmit and be received and frequency synthesis. Tx path of the TRM produces signals of total 28 bandwidths up to 96.8 MHz and has output signal level of more than + 9.37 dBm. Rx path of the TRM has minimum noise figure of 15.7 dB. The measurement results show that required requirements are satisfied. The TRM is installed on the NEXTSat-2 flight model(FM), launched by KSLV-II(Nuri) on May 23, 2023 and currently operational.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_3
• /
• pp.1475-1490
• /
• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2003.10a
• /
• pp.417-422
• /
• 2003

The KOMPSAT-2 MSC(Multi-Spectral Camera), with high spatial resolution, is currently under development and will be launched in the end of 2004. A sensor model relates a 3-D ground position to the corresponding 2-D image position and describes the imaging geometry that is necessary to reconstruct the physical imaging process. The Rational Function Model (RFM) has been considered as a generic sensor model. form. The RFM is technically applicable to all types of sensors such as frame, pushbroom, whiskbroom and SAR etc. With the increasing availability of the new generation imaging sensors, accurate and fast rectification of digital imagery using a generic sensor model becomes of great interest to the user community. This paper describes the procedure to generation of the RPC (Rational Polynomial Coefficients) for KOMPSAT-2 MSC.