• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1958-1960
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• 2001

In this paper, we propose the design methodology of target tracking system using the identification of TS fuzzy model based on genetic algorithm(GA) and RLS algorithm. In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter(EKF), the performance of the system may be deteriorated in highly nonlinear situation. In this paper, to resolve these problems of nonlinear filtering technique, the error of EKF by nonlinearity is compensated by identifying TS fuzzy model. In the proposed method, after composing training datum from the parameters of EKF, by identifying the premise and consequent parameters and the rule numbers of TS fuzzy model using GA, and by tuning finely the consequent parameters of TS fuzzy model using recursive least square(RLS) algorithm, the error of EKF is compensated. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• ETRI Journal
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• v.34 no.2
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• pp.260-263
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• 2012

In this letter, we propose a moving-target tracking algorithm based on a particle filter that uses the time difference of arrival (TDOA)/frequency difference of arrival (FDOA) measurements acquired by distributed sensors. It is shown that the performance of the proposed algorithm, based on the particle filter, outperforms the one based on the extended Kalman filter. The use of both the TDOA and FDOA measurements is shown to be effective in the moving-target tracking. It is proven that the particle filter deals with the nonlinear nature of the movingtarget tracking problem successfully.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.9
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• pp.381-388
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• 2002

This paper suggests the activation-only VSIMM estimator, applied mainly to target tracking problems. This algorithm is much simpler and easier to implement than the ordinary VSIMM algorithm. Also the activation-only VSIMM algorithm provides a substantial reduction in computation while having identical performance with the ordinary VSIMM estimator and the FSIMM estimator. More importantly, the drawbacks related to the improper termination and activation inherent to the VSIMM algorithm are eliminated in this algorithm. The performance of this estimator will be shown through a Monte Carlo simulation for maneuvering target tracking in comparison with the FSIMM and the VSIMM.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.44-53
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• 2008

An interacting multiple model (IMM) estimation algorithm based on the mixing of the predicted state estimates is proposed in this paper for a right continuous jump-linear system model different from the left-continuous system model used to develop the existing IMM algorithm. The difference lies in the modeling of the mode switching time. Performance of the proposed algorithm is compared numerically with that of the existing IMM algorithm for noisy system identification. Based on the numerical analysis, the proposed algorithm is applied to target tracking with a large sampling period for performance comparison with the existing IMM.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.172.5-172
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• 2001

Target Motion Analysis(TMA) for Passive Sonar Systems with bearing-only measurements needs to enhance system observability to improve target tracking performance by ownship maneuvering. However, tracking problem incurred by weak observaility result in slow convergence of the target estimates. On the other hand, active sonar systems do not have problem associated with system observaility. However, it drawback related to system survivability. In this paper, the algorithm that could be used in Active/passive Mixed-Mode Sonar Systems is proposed to analyze maneuvering target motion and to improve TMA performance. The proposed TMA algorithm is tested by a series of computer simulation runs and the results ...

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1973-1974
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• 2006

The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local maxima of a similarity measure between the color histograms of the target and candidate image. However, the mean shift tracking algorithm using only color histograms has a serious defect. It doesn't use the spatial information of the target. Thus, it is difficult to model the target more exactly. And it is likely to lose the target during the occlusions of other objects which have similar color distributions. To deal with these difficulties we use both color information and spatial information of the target. Our proposed algorithm is robust to occlusions and scale changes in front of dynamic, unstructured background. In addition, our proposed method is computationally efficient. Therefore, it can be executed in real-time.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1500-1504
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• 2005

This paper proposes the design methodology of genetic algorithm (GA)-based fuzzy Kalman filter for tracking the maneuvering target. The performance of the standard Kalman Filter (SKF) has been degraded because mismatches between the modeled target dynamics and the actual target dynamics. To solve this problem, we use the method to estimate the increment of acceleration by a fuzzy system using the relation between maneuver filter residual and non-maneuvering one. To optimize the fuzzy system, a genetic algorithm (GA) is utilized and this is then tuned by the fuzzy logic correction. Finally, the tracking performance of the proposed method has been compared with those of the input estimation (IE) technique and the intelligent input estimation (IIE) through computer simulations.

• 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

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.4
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• pp.474-481
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• 2017

This paper presents a multiple target tracking system using Normalized Rayleigh likelihood of amplitude information of target. Although many studies of Radar systems using amplitude information have been studied, they are focused on single target tracking. This paper proposes the multiple target tracking using amplitude information as well as kinematic information from Radar sensor. The amplitude information are applied in generating the association probability of joint probabilistic data association(JPDA) algorithm through the normalized Rayleigh likelihood. It is verified that the proposed system can enhance the track maintenance and tracking accuracy, especially, in the target crossing case.