• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.70.1-70
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• 2001

Recently, research of visual control is getting more essential in robotic application, and acquiring 3D informations from the 2D images is becoming more important with development of vision system. For this application, we propose the effective way of controlling stereo vision tracking system for target tracking and calculating distance between target and camera. In this paper we address improved controller using dual-loop visual servo which is more effective compared with using single-loop visual servo for stereo vision tracking system. The speed and the accuracy for realizing a real time tracking are important. However, the vision processing speed is too slow to track object in real time by using only vision feedback data. So we use another feedback data from controller parts which offer state feedback ...

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.1
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• pp.47-60
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• 2018

In this paper, we proposed a method for eliminating aiming error of unguided anti-tank rocket using improved target tracking. Since predicted fire is necessary to hit moving targets with unguided rockets, a method was proposed to estimate the position and velocity of target using fire control system. However, such a method has a problem that the hit rate may be lowered due to the aiming error of the shooter. In order to solve this problem, we used an image-based target tracking method to correct error caused by the shooter. We also proposed a robust tracking method based on TLD(Tracking Learning Detection) considering characteristics of the FCS(Fire Control System) devices. To verify the performance of our proposed algorithm, we measured the target velocity using GPS and compared it with our estimation. It is proved that our method is robust to shooter's aiming error.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.145-148
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• 1996

In moving object tracking based on the visual sensory feedback, a prerequisite is to determine which feature or which object is to be tracked and then the feature or the object identification precedes the tracking. In this paper, we focus on the object identification not image feature identification. The target identification is realized by finding out corresponding line segments to the hypothesized model segments of the target. The key idea is the combination of the Mahalanobis distance with the geometrica relationship between model segments and extracted line segments. We demonstrate the robustness and feasibility of the proposed target identification algorithm by a moving vehicle identification and tracking in the video traffic surveillance system over images of a road scene.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.254-258
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• 1996

In this paper, we proposed realtime visual tracking system of moving object for 2D target using extended Kalman Filter Algorithm. A targeting marker are recongnized in each image frame and positions of targer object in each frame from a CCD camera while te targeting marker is attached to the tip of the SCARA robot hand. After the detection of a target coming into any position of the field-of-view, the target is tracked and always made to be located at the center of target window. Then, we can track the moving object which moved in inter-frames. The experimental results show the effectiveness of the Kalman filter algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.541-547
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• 1992

The phased array antenna has the ability to perform adaptive sampling by directing the radar beam without inertia in any direction. The adaptive sampling capability of the phased array antenna allows each sampling time interval to be varied for each target, depending on the acceleration of each target at any time. In this paper we design a three-dimensional adaptive tracking algorithm for the phased array radar system with a given set of measurement parameters. The tracking algorithm avoids taking unnecessarily frequent samples, while keeping the angular prediction error within a fraction of antenna beamwidth so that the probability of detection will not be degraded during a track update illuminations. In our algorithm, the target model and the sampling rate are selected depending on the target range and the target maneuver status which is determined by a maneuver detector. A detailed simulation is conducted to test the validity of our tracking algorithm for encounter geometries under various conditions of maneuver.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.355-365
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• 2022

This paper addresses the problem of real-time tracking a high-speed ballistic target. Particle filters can be considered to overcome the nonlinearity in motion and measurement models in the ballistic target. However, it is difficult to apply particle filters to real-time systems because particle filters generally require much computation time. This paper proposes an accelerated particle filter using graphics processing unit (GPU) for real-time ballistic target tracking. The real-time performance of the proposed method was tested and analyzed on a widely-used embedded system. The comparison results with the conventional particle filter on CPU (central processing unit) showed that the proposed method improved the real-time performance by reducing computation time significantly.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.3
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• pp.258-266
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• 2014

We present a system for the real-time visual relative navigation of a fixed-wing unmanned aerial vehicle in a GPS-denied environment. An extended Kalman filter is used to construct a vision-aided navigation system by fusing the image processing results with barometer and inertial sensor measurements. Using a mean-shift object tracking algorithm, an onboard vision system provides pixel measurements to the navigation filter. The filter is slightly modified to deal with delayed measurements from the vision system. The image processing algorithm and the navigation filter are verified by flight tests. The results show that the proposed aerial system is able to maintain circling around a target without using GPS data.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.583-586
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• 1995

In this paper, a nor fuzzy control algorithm for the target tracking system is proposed, and its characteristics are analyzed and compared with those of the traditional PID controller. Fuzzy rules are generated experimentally using Mamdani's minimum operation and the center of area method. The experimental results prove that the proposed fuzzy algorithm is excellent in our tracking system and its performance is superior to that of the PID controller.

• Journal of Communications and Networks
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• v.15 no.2
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• pp.164-172
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• 2013

We consider the problem of secure and robust clustering for quantized target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose a new method for jointly activating the best group of candidate sensors that participate in data aggregation, detecting the malicious sensors and estimating the target position. Firstly, we select the appropriate group in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a sensor node and a cluster head. Secondly, we detect the malicious sensor nodes based on the information relevance of their measurements. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The selection of the candidate sensors group is based on multi-criteria function, which is computed by using the predicted target position provided by the QVF algorithm, while the malicious sensor nodes detection is based on Kullback-Leibler distance between the current target position distribution and the predicted sensor observation. The performance of the proposed method is validated by simulation results in target tracking for WSN.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.855-861
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• 2007

In real system application, the IMM-based position tracking algorithm requires robust performance, less computing resources and easy design procedure with respect to the uncertain target maneuvering, To solve these problems, an adaptive fuzzy interacting multiple model (AFIMM) algorithm, which is based on the well-defined basis sub-models and well-adjusted mode transition probabilities (MTPs), is proposed. Simulation results show that the proposed algorithm effectively solves the problems in the real system application of the IMM-based position tracking algorithm.