• Journal of Auto-vehicle Safety Association
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• v.12 no.3
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• pp.34-42
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• 2020

ADAS (Advanced Driver Assistance Systems) uses sensors such as camera, radar, lidar and GPS (Global Positioning System). Among these sensors, the camera has many advantages compared with other sensors. The reason is that it is cheap, easy to use and can identify objects. In this paper, therefore, a theoretical formula was proposed to obtain the distance from the vehicle's front wheel to the lane using a monocular camera. And the validity of the theoretical formula was verified through the actual vehicle test. The results of the actual vehicle test in scenario 4 resulted in a maximum error of 0.21 m. The reason is that it is difficult to detect the lane in the curved road, and it is judged that errors occurred due to the occurrence of significant yaw rates. The maximum error occurred in curve road condition, but the error decreased after lane return. Therefore, the proposed theoretical formula makes it possible to assess the safety of the LKA system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.139-144
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• 2005

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion. have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.5
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• pp.447-454
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• 2016

Increase of space debris makes low earth orbit(LEO) environment more complex day by day and space situation Awareness(SSA) is becoming more important. As an essential part of SSA, space object surveillance and tracking is studied by many countries including America and Europe. And radar system forms the backbone of an space surveillance and tracking. Currently, Korea operates many LEO satellites like KOMPSAT but does not have dedicated radar systems which provide collision surveillance between satellite and space debris. Korea Aerospace Research Institute(KARI) NARO space center operates launch-vehicle tracking radar system in GOHEUNG and JEJU, respectively. In this paper, we describe developing operation concept to track International Space Station(ISS) using NARO radar and results of tracking. Then, we describe ISS orbit determination using radar tracking data. Lastly, orbit determination result is compares with TLE for analyzing effectiveness of orbit determination.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.517-522
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• 2023

Existing radars have been developed by applying RF sub-array algorithms, and recently, fully digital Multiple-Input Multiple-Output (MIMO) radar algorithms have been implemented for vehicle radars. In this paper, the radar algorithm applying the Phased MIMO method to the hardware of the RF sub-array method, which is an unsecured technology, was implemented and verified in real time. In order to secure RF sub-array Phased MIMO algorithm technology, a hardware structure for FPGA-based real-time signal processing was presented, and performance was first predicted through design and simulation. Through this, the digital signal of FPGA-based broadband MIMO FMCW radar The processing hardware was developed, and the Phased MIMO radar algorithm of the RF sub-Array method was finally implemented and verified in real time. Based on this, it is judged that it will be possible to secure and apply core technologies necessary for terahertz band radar in the future.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.293-302
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• 2014

Radar systems are used in remote sensing mainly as space-borne, airborne and ground-based Synthetic Aperture Radar (SAR), scatterometer and Doppler radar. Those systems are composed of expensive equipments and require expertise and professional skills for operation. Because of the limitation in getting experiences of the radar and SAR systems and its operations in ordinary universities and institutions, it is difficult to learn and exercise essential principles of radar hardware which are essential to understand and develop new application fields. To overcome those difficulties, in this paper, we present the construction and experiment of a low-cost educational radar system based on the blueprints of the MIT Cantenna system. The radar system was operated in three modes. Firstly, the velocity of moving cars was measured in Doppler radar mode. Secondly, the range of two moving targets were measured in radar mode with range resolution. Lastly, 2D images were constructed in GB-SAR mode to enhance the azimuth resolution. Additionally, we simulated the SAR raw data to compare Deramp-FFT and ${\omega}-k$ algorithms and to analyze the effect of antenna positional error for SAR focusing. We expect the system can be further developed into a light-weight SAR system onboard a unmanned aerial vehicle by improving the system with higher sampling frequency, I/Q acquisition, and more stable circuit design.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.9
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• pp.883-889
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• 2009

Since the SRRs(Short Range Radar) load on the GVESs(Ground Vehicle Equipment System) are operated within several hundred meters, it is important to suppress the interferences between the SRRs. These interference are reduced using the frequency separation between channels, however this method isn't a perfect solution owing to a difficulty of a realization for the interference suppression. Thus, a pulse-to-pulse coding used to suppress the interference fundamentally is proposed in this paper. The concept, the application method and the test results of the proposed method have been described in this paper.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.189-196
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• 2024

Classification of drones and birds is challenging due to diverse flight patterns and limited data availability. Previous research has focused on identifying the flight patterns of unmanned aerial vehicles by emphasizing dynamic features such as speed and heading. However, this approach tends to neglect crucial spatial information, making accurate discrimination of unmanned aerial vehicle characteristics challenging. Furthermore, training methods for situations with imbalanced data among classes have not been proposed by traditional machine learning techniques. In this paper, we propose a data processing method that preserves angle information while maintaining positional details, enabling the deep learning model to better comprehend positional information of drones. Additionally, we introduce a training technique to address the issue of data imbalance.

• Journal of IKEEE
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• v.28 no.3
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• pp.397-404
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• 2024

This paper presents the design and implementation results of a Raspberry-Pi-based embedded system with an FPGA accelerator that can detect and classify objects using an FMCW radar sensor for preventing door collision accidents in vehicles. The proposed system performs a radar sensor signal processing and a deep learning processing that classifies objects into bicycles, automobiles, and pedestrians. Since the CNN algorithm requires substantial computation and memory, it is not suitable for embedded systems. To address this, we implemented a lightweight deep learning model, BNN, optimized for embedded systems on an FPGA, and verified the results achieving a classification accuracy of 90.33% and an execution time of 20ms.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.164-164
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• 2000

This paper proposes a moving window technique that extracts lanes and vehicles using the images captured by a CCD camera equipped inside an automobile in real time. For the purpose, first of all the optimal size of moving window is determined based upon speed of the vehicle, road curvature, and camera parameters. Within the moving windows that are dynamically changing, lanes and vehicles are extracted, and the vehicles within the driving lanes are classified as obstacles. Assuming highway driving, there are two sorts of image-objects within the driving lanes: one is ground mark to show the limit speed or some information for driving, and the other is the vehicle as an obstacle. Using characteristics of three-dimension objects, a neural network can be trained to distinguish the vehicle from ground mark. When it is recognized as an obstacle, the distance from the camera to the front vehicle can be calculated with the aids of database that keeps the models of automobiles on the highway. The correctness of this measurement is verified through the experiments comparing with the radar and laser sensor data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.232-237
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• 2009

Active safety vehicle devices are getting more attention recently. To prevent traffic accidents, the environment in front and even around the vehicle must be checked and monitored. In the present applications, mainly camera and radar based systems are used as sensing devices. Laser scanner, one of the sensing devices, has the advantage of obtaining accurate measurement of the distance and the geometric information about the objects in the field of view of the laser scanner. However, there is a problem that detecting object occluded by a foreground one is difficult. In this paper, criterions are proposed to manage this problem. Simulation is conducted by vehicle mounted the laser scanner and multiple-hypothesis algorithm tracks the candidate objects. We compare the running times as multi-hypothesis algorithm parameter varies.