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Extreme Environment Rotated Object Detection Network
Giljun Lee, Junyaup Kim, Gwanghan Lee, Simon S. Woo
http://doi.org/10.5626/JOK.2023.50.11.966
With the advancement of object detection models, it is possible to efficiently infer synthetic aperture radar (SAR) and electro-optical (EO) satellite images. However, conventional object detection models using horizontal bounding boxes (HBB) struggle to detect small and densely grouped objects in satellite images. To address this issue, this paper proposes E^2RDet. This algorithm effectively modifies the structure of the Yolov7 object detection model, enabling it to accurately detect objects represented by oriented bounding boxes (OBB) in SAR images. This algorithm improves the object detection model architecture and loss function to facilitate learning of an object"s dynamic (orientation) posture. Using various training datasets, E^2RDet demonstrates performance improvements across three benchmark SAR datasets. This indicates that existing HBB object detection models can train and perform object detection on objects represented by OBBs.
Radar Signal Processor for High-Resolution Target Detection
http://doi.org/10.5626/JOK.2022.49.5.369
Recently, as the technology of multi-function radar is developed, the radar deception technology of ballistic missiles is also developing. For some ballistic missiles, the propellant explodes in the air after the stage is separated, causing the warhead and many fragments to fly together, which lowers the multi-function radar’s ability to engage ballistic missiles. Thus, there is a need for a radar system capable of operating a broadband waveform to intercept a warhead, by quickly discriminating between a high-speed warhead and fragments while retaining the existing target detection/tracking function. It is possible to find and intercept warhead among fragments by extracting the length of a target, using a broadband waveform and performing warhead classification using this. In this paper, we describe the process of performing the target detection/tracking function using a narrowband waveform such as doppler processing, pulse compression, threshold processing, and target processing and high-resolution target length extraction and phase diffraction correction for accurate length extraction using a wideband waveform to create a radar system that satisfies these requirements. Also, it shows the results of designing and implementing these functions with signal processing software and performing tests.
The Design of a Multi-Function Radar Simulator for the Identification of Friend or Foe(IFF) in the Mode-5 Product Improvement Program
Younghwan Jeong, Chansu Kim, Jungin Oh, Wonsik Lee, Sounghyouk Wi
http://doi.org/10.5626/JOK.2020.47.6.622
Identification of Friend or Foe(IFF) is a function of military surveillance system used to identify whether the monitored object is an ally or an enemy, these system are installed in fighters, ships, and interceptors among others. The United States plans to suspend the Mode-4 identification system and apply Mode-5 from July 2020 forward. The transition to Mode-5 is inevitable as it ensures the interoperability of peer identification systems used in the Republic of Korea’s military operations with the US as well as other NATO member states. If the IFF function found in control centers and multi-function radars is changed in regional air defense weapons systems, revalidation of these weapons systems is required to ensure stability and correct function. Therefore, a multi-function radar simulator is vital use in interface verification, unit tests, and integrated tests before evaluation of the new systems can be completed. This paper presents the design of a simulator for mode-5 performance testing and improvement.
Automatic Recognition Algorithm of Unknown Ships on Radar
Hyun Chul Jung, Soung Woong Yoon, Sang Hoon Lee
Seeking and recognizing maritime targets are very important tasks for maritime safety. While searching for maritime targets using radar is possible, recognition is conducted without automatic identification system, radio communicator or visibility. If this recognition is not feasible, radar operator must tediously recognize maritime targets using movement features on radar base on know-how and experience. In this paper, to support the radar operator’s mission of continuous observation, we propose an algorithm for automatic recognition of an unknown ship using movement features on radar and a method of detecting potential ship related accidents. We extract features from contact range, course and speed of four types of vessels and evaluate the recognition accuracy using SVM and suggest a method of detecting potential ship related accidents through the algorithm. Experimentally, the resulting recognition accuracy is found to be more than 90% and presents the possibility of detecting potential ship related accidents through the algorithm using information of MV Sewol. This method is an effective way to support operator’s know-how and experience in various circumstances and assist in detecting potential ship related accidents.
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