• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.126-130
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• 2001

In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion (FBFE) based on virus evolutionary genetic algorithm(VEGA). 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. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FBFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by identifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. 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.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.306-314
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• 2013

Automatic tracking in cluttered environment requires the initiation and maintenance of tracks, and track existence probability of true track is kept by Markov Chain Two model of target existence propagation. Unlike Markov Chain One model for target existence propagation, Markov Chain Two model is made up three hypotheses about target existence event which are that the target exist and is detectable, the target exists and is non-detectable through occlusion, and the target does not exist and is non-detectable according to non-existing target. In this paper we present multi-scan single target tracking algorithm based on the target existence, which call the Integrated Track Splitting algorithm with Markov Chain Two model in imaging sensor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.908-928
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• 2022

Multiple target tracking mainly focuses on tracking unknown number of targets in the complex environment of clutter and missed detection. The generalized labeled multi-Bernoulli (GLMB) filter has been shown to be an effective approach and attracted extensive attention. However, in the scenarios where the clutter rate is high or measurement-outliers often occur, the performance of the GLMB filter will significantly decline due to the Gaussian-based likelihood function is sensitive to clutter. To solve this problem, this paper presents a robust GLMB filter and smoother to improve the tracking performance in the scenarios with high clutter rate, low detection probability, and measurement-outliers. Firstly, a Student-T distribution variational Bayesian (TDVB) filtering technology is employed to update targets' states. Then, The likelihood weight in the tracking process is deduced again. Finally, a trajectory smoothing method is proposed to improve the integrative tracking performance. The proposed method are compared with recent multiple target tracking filters, and the simulation results show that the proposed method can effectively improve tracking accuracy in the scenarios with high clutter rate, low detection rate and measurement-outliers. Code is published on GitHub.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.424-429
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• 1992

A practical adaptive tracking filter for a maneuvering target is proposed in this paper by combining a modified input estimation technique with pseudo-residuals and a decoupled tracking filter in line-of-sight Cartesian coordinate system. Since the adaptive tracking filter has decoupled structure and computes maneuver input estimates for each axis separately, it requires much less computations compared with the coventional tracking filter with MIE technique without degrading performance. Also, since pseudo-measurement noises in line-of-sight Cartesian coordinate system are much less correlated compared with those of inertial Cartesian coordinate system, the proposed tracking filter produces less false alarms or miss detections to improve the performance.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.524-527
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• 1997

Conventional target tracking algorithms based on the linear estimation techniques perform quite efficiently when the target motion does not involve maneuvers. Target maneuvers involving short term accelerations, however, cause a bias in the measurement sequence. Accurate compensation for the bias requires processing more samples of which adds to the computational complexity. The primary motivation for employing a neural network for this task comes from the efficiency with which more features can be as inputs for bias compensation. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates and hence can take the burden off the primary Kalman filter which still provides the target position and velocity estimates.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.3
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• pp.354-359
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• 2009

In this paper, the Autocoast algorithm is proposed for EOTS to overcome the target occlusion status. Coast mode, one of tracking modes, is to maintain the servo slew rate with the tracking rate right before the loss of track. The Autocoast algorithm makes decision of entering coast mode by the prediction of target occlusion and tries to refind target after the coast time. This algorithm composes of 3 steps, the first step is the prediction process of the occlusion by target-like background, the second one is the check process of the occlusion happened after background intensity variation, and the last one is the process of refinding target. The result of computer simulation, test under laboratory, and real test with EOTS shows the applicability for the automatic video tracking system.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.3
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• pp.352-361
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• 2017

This paper proposes a design and fabrication of the test equipment that is implemented as a part of the airborne tracking radar inspection under the environment of indoor simulation. This test equipment provides controlling the operation status of airborne tracking radar and replicating the velocity and range information of target by generating a variety of target signal. This is mainly composed of radar operation controller, target signal generator, horn antenna driving devices. Radar operation controller is able to perform the controlling of radar operation mode and monitoring in real time by serial communication. Target signal generator is generated doppler signal and range delayed signal using virtual target of RF-band. Horn antenna driving devices perform a role of target simulating exercise. In the end, the performance is demonstrated using experiment results of test equipment for airborne tracking radar.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2340-2344
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• 2003

This paper considers a tracking filter algorithm which can track a maneuvering target. Multiresolutional Interacting Multiple Model (MRIMM) algorithm is proposed to reduce computational burden. In this paper multiresolutional state space model equation and multiresolutional measurement equation are derived by using wavelet transform. This paper shows the outline of MRIMM algorithm. Simulation results show that MRIMM algorithm maintains a good tracking performance and reduces computational burden.

• Journal of Electrical Engineering and information Science
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• v.2 no.4
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• pp.23-27
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• 1997

Presented are closed-form expressions of the three-state exponentially correlated acceleration (ECA) target-tracking filter. The steady-state solution is derived based on Vaughan's approach for the case that he measurements of target position and velocity are available at discrete point in time. The solution for ECA tracking filter using only position measurements and the solution for the constant acceleration (CA) tracking filter are obtained as a special case of the presented results.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.438-449
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• 2005

In this paper, we describes the information-theoretic approaches to sensor selection and sensor placement in sensor net­works for target localization and tracking. We have developed a sensor selection heuristic to activate the most informative candidate sensor for collaborative target localization and tracking. The fusion of the observation by the selected sensor with the prior target location distribution yields nearly the greatest reduction of the entropy of the expected posterior target location distribution. Our sensor selection heuristic is computationally less complex and thus more suitable to sensor networks with moderate computing power than the mutual information sensor selection criteria. We have also developed a method to compute the posterior target location distribution with the minimum entropy that could be achieved by the fusion of observations of the sensor network with a given deployment geometry. We have found that the covariance matrix of the posterior target location distribution with the minimum entropy is consistent with the Cramer-Rao lower bound (CRB) of the target location estimate. Using the minimum entropy of the posterior target location distribution, we have characterized the effect of the sensor placement geometry on the localization accuracy.