• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.120-124
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• 1991

In this paper, we consider the maneuvering detection and target tracking problem in uncertain linear discrete-time systems. The maneuvering detection is based on X$^{2}$ test[2,71, where Kalman filters have been utilized so far. The target tracking is performed by the maneuvering input compensation based on a maximum likelihood estimator. KF has been known to diverge when some modelling errors exist and fail to detect the maneuvering and to track the target in uncertain systems. Thus this paper adopt the FIR filter[l], which is known to be robust to modelling errors, for maneuvering detection and target tracking problem. Various computer simulations show the superior performance of the FIR filter in this problem.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.5
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• pp.76-84
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• 1994

Recently, Optical BJTC as a new approach for real-time multi-target tracking has been intensively studied. But the conventional system has some problems in the practical applications such as the false alarm and target missing and low correlation efficiency, and these poor performances are analyzed to be deeply dependent on the binarization method. So, in this paper, a new BJTC system which has the improved performances in target discrimination and diffraction efficiency is suggested, which is based on the JTPS having the same properties with those of the matched filter and new power spectrum binarization method to use effectively the high frequency components of the JTPS signal. Through the computer simulation and some experiments, the performances of the new BJTC tracking system are analyzed and proved to be superior to those of the conventional system baseds on Median method in multi- target tracking problems.

• Journal of Sensor Science and Technology
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• v.18 no.3
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• pp.190-196
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• 2009

In a conventional camera system, a target tracking system consists of a camera part and a image processing part. However, in the field of the real time image processing, the vision chip for edge detection which was made by imitating the algorithm of humanis retina is superior to the conventional digital image processing systems because the human retina uses the parallel information processing method. In this paper, we present a high speed target tracking system using the function of the CMOS vision chip for edge detection.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.110-113
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• 2022

Non-playable game character plays an important role in improving the concentration of the game and the interest of the user, and recently implementation of NPC with reinforcement learning has been in the spotlight. In this paper, we estimate an AI target tracking method via reinforcement learning, and implement an AI-based tracking agency of specific target object with avoiding traps through Unity ML-Agents Toolkit. The implementation is built in Unity game engine, and simulations are conducted through a number of experiments. The experimental results show that outstanding performance of the tracking target with avoiding traps is shown with good enough results.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1740-1741
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• 2011

This paper proposes a new linear recursive target state estimator for automotive collision warning system. The target motion is modeled in Cartesian coordinate system while the radar measurements such as range, line-of-sight angle and range rate are obtained in polar coordinate system. To solve the problem by nonlinear relation between these two coordinate system, a practical linear filter design scheme employing the predicted line-of-sight Cartesian coordinate system (PLCCS) is proposed. Especially, PLCCS can effectively incorporate range rate measurements into target tracking system. It is known that the utilization of range rate measurements enables the improvement of target tracking performance. Moreover, PLCCS based target tracking system is implemented by linear recursive filter structure and hence is more suitable scheme for the development of reliable collision warning system. The performance of the proposed method is demonstrated by computer simulations.

• Journal of Mechanical Science and Technology
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• v.19 no.9
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• pp.1742-1752
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• 2005

This paper presents a Multiple Target Tracking (MTT) Adaptive Cruise Control (ACC) system which consists of three parts; a multi-model-based multi-target state estimator, a primary vehicular target determination algorithm, and a single-target adaptive cruise control algorithm. Three motion models, which are validated using simulated and experimental data, are adopted to distinguish large lateral motions from longitudinally excited motions. The improvement in the state estimation performance when using three models is verified in target tracking simulations. However, the performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. The MTT-ACC system is tested under lane changing situations to examine how much the system performance is improved when multiple models are incorporated. Simulation results show system response that is more realistic and reflective of actual human driving behavior.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.4
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• pp.561-568
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• 2010

Instrumentation radar in a test range has an important role to measure target's TSPI(time, space, position, information). It is well known that it tracks a target stably using a beacon mode. But it may fail to track a target in a certain region using a beacon mode. In this paper, we modeled a simple missile shape similar to ATCMS with two beacon antenna and analyzed an antenna radiation pattern using MLFMM(Multi Level Fast Multipole Method) method. Using the analyzed result of the radiation pattern of the antenna and the attitude data of target, we simulated beacon tracking performance of an instrumentation radar. As a result of simulation, we showed that an instrumentation radar may lose the target because it tracks a area of the beacon antenna pattern.

• ETRI Journal
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• v.38 no.5
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• pp.1019-1029
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• 2016

The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target-tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement-driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre-filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement-driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.8
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• pp.71-78
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• 2006

When a tracking filter may be designed in the Cartesian coordinate, the covariance of the measurement errors varies according to the range and the bearing of an interested target. In this paper, interacting multiple model based tracking filter is formulated in the Cartesian coordinate utilizing the analytic solution of the steady state Kalman filter, which can be able to consider the variation of the measurement error covariance. 100 Monte Carlo runs performed to verify the proposed method. The performance of the proposed method is compared with the conventional fixed gain and Kalman filter based IMM tracking filter in terms of the root mean square error. The simulation results show that the proposed approach meaningfully reduces the computation time and provides a similar tracking performance in comparison with the conventional Kalman filter based IMM tracking filter.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.3
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• pp.22-28
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• 2007

The STIR(Signal Tracking & Illumination Radar) in KDX(Korean Destroyer Experimental) combat system acquires target from designating 3-D target information of surveillance radar (MW-08), and The performance of radar is decided by target acquisition time and accuracy of tracking loop because the STIR tracks automatically in accordance with tracking algorithm. In the view of ship, elements related with target acquisition time of the STIR can be various. In this paper the target acquisition time of the STIR is reduced by identifying the elements and suggesting the performance improvement method. The way of performance improvement is suggested through analysing main affecting factors. First, tracking algorism is required for analysis. Second, fitness of parameters that control elements related with acquisition distance is analyzed. And the third, accuracy of ship based sensors is analyzed. In conclusion, acquisition time against ship target can be advanced to 3 seconds from 10 seconds.