• The Journal of the Acoustical Society of Korea
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• v.22 no.1E
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• pp.19-25
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• 2003

Ryu et al. recently proposed a multiple target angle-tracking algorithm without a data association problem. This algorithm, however, shows the degraded performance on evasive maneuvering targets, because the estimated signal subspace is d,:graded in the algorithm. In this Paper, we proposed a new algorithm, in which VFF-PASTd (Variable Forgetting Factor PASTd) algorithm is applied to Ryu's algorithm to effectively handle the evasive target tracking with better time-varying signal subspace.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.4 s.19
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• pp.5-12
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• 2004

The tracking algorithm based on the interacting multiple model(IMM) requires a considerable number of sub-models for the various maneuvering targets in order to have a good performance. But it is not feasible to use the nm algorithm in the real system because of the computational burden. Therefore, we need an algorithm which requires less computing resources while maintaining a good performance. In this paper, we propose a fuzzy interacting multiple model algorithm(FIMMA) for the tracking of maneuvering targets, which uses a minimal number of sub-models by considering the maneuvering properties and adjusts the mode transition probabilities by using the mode probability as a fuzzy input. In order to verify the performance of FIMMA, the developed algorithm is applied to the tracking of i borne targets. Simulation results show that the FIMMA is very effective in the tracking of maneuvering targets.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.517-524
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• 2015

In this paper, we propose a new target tracking filter architecture using active and passive sensors in underwater environment. A passive sensor for target tracking needs a bearing measurement of target. And target tracking filter for using passive sensor has the observability problem. On the other hand, an active sensor does not have the problem associated with system observability problem because an active sensor uses bearing and range measurement. In this paper, the tracking filter algorithm that could be used in the active and passive sensor system is proposed to analyze maneuvering target and to improve target tracking performance. The proposed tracking filter algorithm is tested by a series of computer simulation runs and the results are analyzed and compared with existing algorithm.

• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.1
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• pp.25-32
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• 2011

Measurement noise variance of the radar is one of the main inputs of a state estimator of surveillance data processing system for air traffic control and has influences on the accuracy performance of maneuvering target tracking. A method is presented of estimating measurement noise variances every frame of target tracking using likelihood functions of multiple IMM filter. The results by running of Monte Carlo simulation show that variances are estimated within 5% of errors compared with true values and the tracking accuracy performance is improved.

• Journal of Navigation and Port Research
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• v.41 no.5
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• pp.297-302
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• 2017

The tracking filter plays a key role in accurate estimation and prediction of maneuvering the vessel's position and velocity. Different methods are used for tracking. However, the most commonly used method is the Kalman filter and its modifications. The ${\alpha}-{\beta}-{\gamma}$ filter is one of the special cases of the general solution provided by the Kalman filter. It is a third order filter that computes the smoothed estimates of position, velocity, and acceleration for the nth observation, and predicts the next position and velocity. Although found to track a maneuvering target with good accuracy than the constant velocity ${\alpha}-{\beta}$ filter, the ${\alpha}-{\beta}-{\gamma}$ filter does not perform impressively under high maneuvers, such as when the target is undergoing changing accelerations. This study aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The ${\alpha}-{\beta}-{\gamma}$ filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration to improve the filter's performance. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model, ${\alpha}-{\beta}-{\gamma}-{\eta}$ algorithm as compared to the constant acceleration model, ${\alpha}-{\beta}-{\gamma}$ in terms of error reduction and stability of the filter during target maneuver.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.499-501
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• 2005

When the target maneuver occurs, the estimate of the standard Kalman filter is biased and its performance may be seriously degraded. To solve this problem, this paper proposes a new intelligent estimation algorithm for a maneuvering target. This algorithm is to estimate the unknown target maneuver by a fuzzy system using the relation between the filter residual and its variation. The detected acceleration input is regarded as an additive process noise. To optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. And then, the modified filter is corrected by the new update equation method using the fuzzy system. The tracking performance of the proposed method is compared with those of an interacting multiple model (IMM).

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.82-89
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• 2012

This paper presents the intelligent tracking algorithm for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. Fuzzy c-mean clustering and predicted impact point are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by fuzzy c-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. The filtering process in a series of the algorithm which estimates the target value recognize the nonlinear maneuvering target as linear one because the filter recognize only remained noise by extracting acceleration from the positional error. After filtering process, we get the estimates target by compensating extracted acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. To maximize the effectiveness of the proposed system, we construct the multiple model structure. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.133-138
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• 2016

This paper describes the track initiation and target-tracking filter for ship tracking in a marine environment by using Light Detection And Ranging (LiDAR). LiDAR with three-dimensional scanning capability is more useful for target tracking in the short to medium range compared to RADAR. LiDAR has rotating multi-beams that return point clouds reflected from targets. Through preprocessing the cluster of the point cloud, the center point can be obtained from the cloud. Target tracking is carried out by using the center points of targets. The track of the target is initiated by investigating the normalized distance between the center points and connecting the points. The regular track obtained from the track initiation can be maintained by the target-tracking filter, which is commonly used in radar target tracking. The target-tracking filter is constructed to track a maneuvering target in a cluttered environment. The target-tracking algorithm including track initiation is experimentally evaluated in a sea-trial test with several boats.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1858-1859
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• 2006

This paper presents a new fuzzy-neural-network based interacting multiple model (FNNBIMM) algorithm for tracking a maneuvering target. To effectively handle the unknown target acceleration, this paper regards it as additional noise, time-varying variance to target model. Each sub model characterized by the variance of the overall process noise, which is obtained on the basis of each acceleration interval. Since it is hard to approximate this time-varying variance adaptively owing to the unknown acceleration, the FNN is utilized to precisely approximate this time-varying variance. The gradient descendant method is utilized to optimize each FNN. To show the feasibility of the proposed algorithm, a numerical example is provided.