• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.06a
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• pp.573-575
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• 2012

기존 RADAR 기반의 VTS에 AIS를 연계 집약하면서 예측 불가능한 데이터 전송률에도 동일선박으로부터의 AIS 및 RADAR 데이터는 상관관계를 유지하면서 물표에 대한 Tracking이 지속적으로 이루어져야 하지만 AIS 신호 Lost시 RADAR Tracking 자동 전환이 안 되는 경우가 많이 발생하고 있다. 또한 3개의 VTS 모니터 화면에 각각 다른 Scale과 다른 관제구역이 디스플레이 됨으로써 특히 모니터 가장자리 부근의 관제구역은 사각지대로 관제사의 집중도가 떨어질 수밖에 없다. 이러한 문제점들은 관제사의 Traffic Image구성 및 Situational Awareness를 방해하는 요소로 작용하며 사고의 개연성을 높이고 있다. 본 연구에서는 VTS 모니터상의 화면 재구성 방법을 통해서 관제사의 SA를 돕고, AIS-RADAR Tracking 알고리즘 보완을 통한 Target Tracking의 안정성을 확보하고, 교육 훈련을 통해서 AIS특성과 Error현상에 대한 관제사들이 충분히 이해하도록 하여 관제업무의 향상을 기하는 방안을 제시하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.185-190
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• 2023

Recently, tracking radar requires the development of millimeter wave tracking radar to acquire target information with high resolution in various environments. The development of millimeter wave tracking radar requires the development of transmission and receiving components that can be applied to the millimeter wave tracking radar, as well as verification of the applicability of the tracking radar. In order to verify the applicability of the developed transmitting and receiving components, it is necessary to develop a signal acquisition device that can control the transmitting and receiving components using the operating concept of a tracking radar and check the status of the received signal. In this paper, we implemented a signal acquisition device that can confirm the applicability of components developed for millimeter wave tracking radar. The signal acquisition device was designed to process in real time the OOOMHz center frequency and OOMHz bandwidth signals input from 4 channels to verify the received signal. In addition, component control applying the tracking radar operation concept was designed to be controlled by communication such as RS422, RS232, and SPI and generation of control signals for the transmission and receiving time. Lastly, the implemented signal acquisition device was verified through a signal acquisition device performance test.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.303-306
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKF uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.1
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• pp.16-24
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• 2011

Automotive Radar Systems are currently under development for various applications to increase accuracy and reliability. The target tracking is most important in single or multiple target environments for accuracy. The tracking algorithm provides smoothed and predicted data for target position and velocity(Doppler). To this end, the fixed gain filter(${\alpha}{\beta}$ filter, ${\alpha}{\beta}{\gamma}$ filter) and dynamic filter(Kalman filter, Singer-Kalman filter, etc) are commonly used. Gating is used to decide whether an observation is assigned to an existing track or new track. Gating algorithms are normally based on computing a statistical error distance between an observation and prediction. The data association takes the observation-to-track pairings that satisfied gating and determines which observation-to-track assignment will actually be made. For data association, NNPDA(Nearest Neighbor Probabilistic Data Association) algorithm is proposed. In this paper, we designed a target tracking system developed for an Automotive Radar System. We show the experimental results of the 77GHz FMCW radar sensor on the roads. Four tracking algorithms(${\alpha}{\beta}$ filter, ${\alpha}{\beta}{\gamma}$ filter, 2nd order Kalman filter, Singer-Kalman filter) have been compared and analyzed to evaluate the performance in test scenario.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.63-73
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• 2021

The radar operated to detect/track aircraft targets is divided into a search radar that operates while the antenna rotating device rotates for the purpose of detecting the target according to the mission characteristics, and a tracking radar that periodically steers and tracks a beam to the predicted position of the target. The tracking radar has a shorter target information acquisition preiod than the search radar. Due to this characteristic, the tracking accuracy is better than that of the search radar, but as the prediction error increases due to the speed error at the beginning of the tracking, there are many cases in which tracking fails at the beginning of tracking due to failure to perform beam steering normally. In this paper, in order to solve the above-mentioned problems, we propose an algorithm for improving the accuracy of track initiation using radial velocity measurements in addition to the position of the measured, and confirm the performance of the proposed algorithm by comparing with the two point differential algorithm

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.1
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• pp.23-29
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• 2013

This paper presents a tracking filter of radar beacon system (RBS) for automatic takeoff and landing of an unmanned aerial vehicle. The proposed tracking filter is designed as the decoupled tracking filter to reduce the computational burden. Also, an adaptive estimation method of the measurement error covariance is proposed to provide an improved tracking performance compared to the conventional decoupled tracking filter whenever the accuracy of RBS observations is degraded. 100 times Monte Carlo runs performed to analyze the performance of the proposed tracking filter in case of normal operation and degraded operations, respectively. The simulation results show that the proposed tracking filter provides the improved tracking accuracy in comparison with the conventional decoupled tracking filter.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.529-538
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• 2006

Separating a reentry vehicle into warhead and body is a conventional and efficient means of producing a huge decoy and increasing the kinetic energy of the warhead. This procedure causes the radar to track the body, whose radar cross section is larger, and ignore the warhead, which is the most important part of the reentry vehicle. However, the procedure is difficult to perform using standard tracking criteria. This study presents a novel tracking algorithm by integrating input estimation and modified probabilistic data association filter to solve this difficulty in a clear environment. The proposed algorithm with a new defined association probability in this filter provides a good tracking capability for the warhead ignoring the radar cross section. The simulation results indicate that the errors between the estimated and the warhead trajectories are reduced to a small interval in a short time. Therefore, the radar can produce a beam to illuminate to the right area and keep tracking the warhead all the way. In conclusion, this algorithm is worthy of further study and application.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.405-413
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• 2011

Defense Systems Test Center in ADD supports increasingly various missile test requirements such as higher altitude event, multi target operation and low-altitude, high velocity target tracking. In this paper, we have proposed the development of instrumentation radar tracking status simulator based on virtual reality. This simulator can predict the tracking status and risk of failure using several modeling algorithms. It consists of target model, radar model, environment model and several algorithms includes the multipath interference effects. Simulation results show that the predict tracking status and signal are similar to the test results of the live flight test. This simulator predicts and analyze all of the status and critical parameters such as the optimal site location, servo response, optimal flight trajectory, LOS(Line of Sight). This simulator provides the mission plan with a powerful M&S tool to rehearse and analyze instrumentation tracking radar measurement plan for live flight test at DSTC(Defense Systems Test Center).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.8
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• pp.995-1001
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• 2012

This paper compares the performance of several tracking filters, namely, alpha-beta filter, Kalman filter and TBM tracking filter for ballistic target tracking problem using multi-function radar. Every of three tracking filters suggested was tested on simulator developed in accordance with TBM trajectory and MFR RSP measurement. The result shows the method using TBM tracking filter gives 75.3 % decreased velocity RMS error than alpha-beta filter. After initialization, the RMS error of range and velocity of the proposed filter is also smaller than the Kalman filter. Finally the proposed filter is suitable for high-speed TBM tracking due to the stable angle tracking accuracy.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.40-41
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• 2023

This paper introduces the design of detection, recognition, and tracking algorithms for VTS radar image-based objects. The detection of objects in radar images utilizes artificial intelligence technology to determine the presence or absence of objects, and can classify the type of object using AI technology. Tracking involves the continuous tracking of detected objects over time, including technology to prevent confusion in the movement path. In particular, for land-based radar, there are unnecessary areas for detection depending on the terrain, so the function of detecting and recognizing vessels within the region of interest (ROI) set in the radar image is included. In addition, the extracted coordinate information is designed to enable various applications and interpretations by calculating speed, direction, etc.